wow-inequalities/final_document/meeting_eoy.html

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<title>Scoping Review: Preliminary findings</title>
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font-display: block;
font-family: "bootstrap-icons";
src: url(data:font/woff;base64,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
}
.bi::before,
[class^="bi-"]::before,
[class*=" bi-"]::before {
display: inline-block;
font-family: bootstrap-icons !important;
font-style: normal;
font-weight: normal !important;
font-variant: normal;
text-transform: none;
line-height: 1;
vertical-align: -.125em;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}
.bi-123::before { content: "\f67f"; }
.bi-alarm-fill::before { content: "\f101"; }
.bi-alarm::before { content: "\f102"; }
.bi-align-bottom::before { content: "\f103"; }
.bi-align-center::before { content: "\f104"; }
.bi-align-end::before { content: "\f105"; }
.bi-align-middle::before { content: "\f106"; }
.bi-align-start::before { content: "\f107"; }
.bi-align-top::before { content: "\f108"; }
.bi-alt::before { content: "\f109"; }
.bi-app-indicator::before { content: "\f10a"; }
.bi-app::before { content: "\f10b"; }
.bi-archive-fill::before { content: "\f10c"; }
.bi-archive::before { content: "\f10d"; }
.bi-arrow-90deg-down::before { content: "\f10e"; }
.bi-arrow-90deg-left::before { content: "\f10f"; }
.bi-arrow-90deg-right::before { content: "\f110"; }
.bi-arrow-90deg-up::before { content: "\f111"; }
.bi-arrow-bar-down::before { content: "\f112"; }
.bi-arrow-bar-left::before { content: "\f113"; }
.bi-arrow-bar-right::before { content: "\f114"; }
.bi-arrow-bar-up::before { content: "\f115"; }
.bi-arrow-clockwise::before { content: "\f116"; }
.bi-arrow-counterclockwise::before { content: "\f117"; }
.bi-arrow-down-circle-fill::before { content: "\f118"; }
.bi-arrow-down-circle::before { content: "\f119"; }
.bi-arrow-down-left-circle-fill::before { content: "\f11a"; }
.bi-arrow-down-left-circle::before { content: "\f11b"; }
.bi-arrow-down-left-square-fill::before { content: "\f11c"; }
.bi-arrow-down-left-square::before { content: "\f11d"; }
.bi-arrow-down-left::before { content: "\f11e"; }
.bi-arrow-down-right-circle-fill::before { content: "\f11f"; }
.bi-arrow-down-right-circle::before { content: "\f120"; }
.bi-arrow-down-right-square-fill::before { content: "\f121"; }
.bi-arrow-down-right-square::before { content: "\f122"; }
.bi-arrow-down-right::before { content: "\f123"; }
.bi-arrow-down-short::before { content: "\f124"; }
.bi-arrow-down-square-fill::before { content: "\f125"; }
.bi-arrow-down-square::before { content: "\f126"; }
.bi-arrow-down-up::before { content: "\f127"; }
.bi-arrow-down::before { content: "\f128"; }
.bi-arrow-left-circle-fill::before { content: "\f129"; }
.bi-arrow-left-circle::before { content: "\f12a"; }
.bi-arrow-left-right::before { content: "\f12b"; }
.bi-arrow-left-short::before { content: "\f12c"; }
.bi-arrow-left-square-fill::before { content: "\f12d"; }
.bi-arrow-left-square::before { content: "\f12e"; }
.bi-arrow-left::before { content: "\f12f"; }
.bi-arrow-repeat::before { content: "\f130"; }
.bi-arrow-return-left::before { content: "\f131"; }
.bi-arrow-return-right::before { content: "\f132"; }
.bi-arrow-right-circle-fill::before { content: "\f133"; }
.bi-arrow-right-circle::before { content: "\f134"; }
.bi-arrow-right-short::before { content: "\f135"; }
.bi-arrow-right-square-fill::before { content: "\f136"; }
.bi-arrow-right-square::before { content: "\f137"; }
.bi-arrow-right::before { content: "\f138"; }
.bi-arrow-up-circle-fill::before { content: "\f139"; }
.bi-arrow-up-circle::before { content: "\f13a"; }
.bi-arrow-up-left-circle-fill::before { content: "\f13b"; }
.bi-arrow-up-left-circle::before { content: "\f13c"; }
.bi-arrow-up-left-square-fill::before { content: "\f13d"; }
.bi-arrow-up-left-square::before { content: "\f13e"; }
.bi-arrow-up-left::before { content: "\f13f"; }
.bi-arrow-up-right-circle-fill::before { content: "\f140"; }
.bi-arrow-up-right-circle::before { content: "\f141"; }
.bi-arrow-up-right-square-fill::before { content: "\f142"; }
.bi-arrow-up-right-square::before { content: "\f143"; }
.bi-arrow-up-right::before { content: "\f144"; }
.bi-arrow-up-short::before { content: "\f145"; }
.bi-arrow-up-square-fill::before { content: "\f146"; }
.bi-arrow-up-square::before { content: "\f147"; }
.bi-arrow-up::before { content: "\f148"; }
.bi-arrows-angle-contract::before { content: "\f149"; }
.bi-arrows-angle-expand::before { content: "\f14a"; }
.bi-arrows-collapse::before { content: "\f14b"; }
.bi-arrows-expand::before { content: "\f14c"; }
.bi-arrows-fullscreen::before { content: "\f14d"; }
.bi-arrows-move::before { content: "\f14e"; }
.bi-aspect-ratio-fill::before { content: "\f14f"; }
.bi-aspect-ratio::before { content: "\f150"; }
.bi-asterisk::before { content: "\f151"; }
.bi-at::before { content: "\f152"; }
.bi-award-fill::before { content: "\f153"; }
.bi-award::before { content: "\f154"; }
.bi-back::before { content: "\f155"; }
.bi-backspace-fill::before { content: "\f156"; }
.bi-backspace-reverse-fill::before { content: "\f157"; }
.bi-backspace-reverse::before { content: "\f158"; }
.bi-backspace::before { content: "\f159"; }
.bi-badge-3d-fill::before { content: "\f15a"; }
.bi-badge-3d::before { content: "\f15b"; }
.bi-badge-4k-fill::before { content: "\f15c"; }
.bi-badge-4k::before { content: "\f15d"; }
.bi-badge-8k-fill::before { content: "\f15e"; }
.bi-badge-8k::before { content: "\f15f"; }
.bi-badge-ad-fill::before { content: "\f160"; }
.bi-badge-ad::before { content: "\f161"; }
.bi-badge-ar-fill::before { content: "\f162"; }
.bi-badge-ar::before { content: "\f163"; }
.bi-badge-cc-fill::before { content: "\f164"; }
.bi-badge-cc::before { content: "\f165"; }
.bi-badge-hd-fill::before { content: "\f166"; }
.bi-badge-hd::before { content: "\f167"; }
.bi-badge-tm-fill::before { content: "\f168"; }
.bi-badge-tm::before { content: "\f169"; }
.bi-badge-vo-fill::before { content: "\f16a"; }
.bi-badge-vo::before { content: "\f16b"; }
.bi-badge-vr-fill::before { content: "\f16c"; }
.bi-badge-vr::before { content: "\f16d"; }
.bi-badge-wc-fill::before { content: "\f16e"; }
.bi-badge-wc::before { content: "\f16f"; }
.bi-bag-check-fill::before { content: "\f170"; }
.bi-bag-check::before { content: "\f171"; }
.bi-bag-dash-fill::before { content: "\f172"; }
.bi-bag-dash::before { content: "\f173"; }
.bi-bag-fill::before { content: "\f174"; }
.bi-bag-plus-fill::before { content: "\f175"; }
.bi-bag-plus::before { content: "\f176"; }
.bi-bag-x-fill::before { content: "\f177"; }
.bi-bag-x::before { content: "\f178"; }
.bi-bag::before { content: "\f179"; }
.bi-bar-chart-fill::before { content: "\f17a"; }
.bi-bar-chart-line-fill::before { content: "\f17b"; }
.bi-bar-chart-line::before { content: "\f17c"; }
.bi-bar-chart-steps::before { content: "\f17d"; }
.bi-bar-chart::before { content: "\f17e"; }
.bi-basket-fill::before { content: "\f17f"; }
.bi-basket::before { content: "\f180"; }
.bi-basket2-fill::before { content: "\f181"; }
.bi-basket2::before { content: "\f182"; }
.bi-basket3-fill::before { content: "\f183"; }
.bi-basket3::before { content: "\f184"; }
.bi-battery-charging::before { content: "\f185"; }
.bi-battery-full::before { content: "\f186"; }
.bi-battery-half::before { content: "\f187"; }
.bi-battery::before { content: "\f188"; }
.bi-bell-fill::before { content: "\f189"; }
.bi-bell::before { content: "\f18a"; }
.bi-bezier::before { content: "\f18b"; }
.bi-bezier2::before { content: "\f18c"; }
.bi-bicycle::before { content: "\f18d"; }
.bi-binoculars-fill::before { content: "\f18e"; }
.bi-binoculars::before { content: "\f18f"; }
.bi-blockquote-left::before { content: "\f190"; }
.bi-blockquote-right::before { content: "\f191"; }
.bi-book-fill::before { content: "\f192"; }
.bi-book-half::before { content: "\f193"; }
.bi-book::before { content: "\f194"; }
.bi-bookmark-check-fill::before { content: "\f195"; }
.bi-bookmark-check::before { content: "\f196"; }
.bi-bookmark-dash-fill::before { content: "\f197"; }
.bi-bookmark-dash::before { content: "\f198"; }
.bi-bookmark-fill::before { content: "\f199"; }
.bi-bookmark-heart-fill::before { content: "\f19a"; }
.bi-bookmark-heart::before { content: "\f19b"; }
.bi-bookmark-plus-fill::before { content: "\f19c"; }
.bi-bookmark-plus::before { content: "\f19d"; }
.bi-bookmark-star-fill::before { content: "\f19e"; }
.bi-bookmark-star::before { content: "\f19f"; }
.bi-bookmark-x-fill::before { content: "\f1a0"; }
.bi-bookmark-x::before { content: "\f1a1"; }
.bi-bookmark::before { content: "\f1a2"; }
.bi-bookmarks-fill::before { content: "\f1a3"; }
.bi-bookmarks::before { content: "\f1a4"; }
.bi-bookshelf::before { content: "\f1a5"; }
.bi-bootstrap-fill::before { content: "\f1a6"; }
.bi-bootstrap-reboot::before { content: "\f1a7"; }
.bi-bootstrap::before { content: "\f1a8"; }
.bi-border-all::before { content: "\f1a9"; }
.bi-border-bottom::before { content: "\f1aa"; }
.bi-border-center::before { content: "\f1ab"; }
.bi-border-inner::before { content: "\f1ac"; }
.bi-border-left::before { content: "\f1ad"; }
.bi-border-middle::before { content: "\f1ae"; }
.bi-border-outer::before { content: "\f1af"; }
.bi-border-right::before { content: "\f1b0"; }
.bi-border-style::before { content: "\f1b1"; }
.bi-border-top::before { content: "\f1b2"; }
.bi-border-width::before { content: "\f1b3"; }
.bi-border::before { content: "\f1b4"; }
.bi-bounding-box-circles::before { content: "\f1b5"; }
.bi-bounding-box::before { content: "\f1b6"; }
.bi-box-arrow-down-left::before { content: "\f1b7"; }
.bi-box-arrow-down-right::before { content: "\f1b8"; }
.bi-box-arrow-down::before { content: "\f1b9"; }
.bi-box-arrow-in-down-left::before { content: "\f1ba"; }
.bi-box-arrow-in-down-right::before { content: "\f1bb"; }
.bi-box-arrow-in-down::before { content: "\f1bc"; }
.bi-box-arrow-in-left::before { content: "\f1bd"; }
.bi-box-arrow-in-right::before { content: "\f1be"; }
.bi-box-arrow-in-up-left::before { content: "\f1bf"; }
.bi-box-arrow-in-up-right::before { content: "\f1c0"; }
.bi-box-arrow-in-up::before { content: "\f1c1"; }
.bi-box-arrow-left::before { content: "\f1c2"; }
.bi-box-arrow-right::before { content: "\f1c3"; }
.bi-box-arrow-up-left::before { content: "\f1c4"; }
.bi-box-arrow-up-right::before { content: "\f1c5"; }
.bi-box-arrow-up::before { content: "\f1c6"; }
.bi-box-seam::before { content: "\f1c7"; }
.bi-box::before { content: "\f1c8"; }
.bi-braces::before { content: "\f1c9"; }
.bi-bricks::before { content: "\f1ca"; }
.bi-briefcase-fill::before { content: "\f1cb"; }
.bi-briefcase::before { content: "\f1cc"; }
.bi-brightness-alt-high-fill::before { content: "\f1cd"; }
.bi-brightness-alt-high::before { content: "\f1ce"; }
.bi-brightness-alt-low-fill::before { content: "\f1cf"; }
.bi-brightness-alt-low::before { content: "\f1d0"; }
.bi-brightness-high-fill::before { content: "\f1d1"; }
.bi-brightness-high::before { content: "\f1d2"; }
.bi-brightness-low-fill::before { content: "\f1d3"; }
.bi-brightness-low::before { content: "\f1d4"; }
.bi-broadcast-pin::before { content: "\f1d5"; }
.bi-broadcast::before { content: "\f1d6"; }
.bi-brush-fill::before { content: "\f1d7"; }
.bi-brush::before { content: "\f1d8"; }
.bi-bucket-fill::before { content: "\f1d9"; }
.bi-bucket::before { content: "\f1da"; }
.bi-bug-fill::before { content: "\f1db"; }
.bi-bug::before { content: "\f1dc"; }
.bi-building::before { content: "\f1dd"; }
.bi-bullseye::before { content: "\f1de"; }
.bi-calculator-fill::before { content: "\f1df"; }
.bi-calculator::before { content: "\f1e0"; }
.bi-calendar-check-fill::before { content: "\f1e1"; }
.bi-calendar-check::before { content: "\f1e2"; }
.bi-calendar-date-fill::before { content: "\f1e3"; }
.bi-calendar-date::before { content: "\f1e4"; }
.bi-calendar-day-fill::before { content: "\f1e5"; }
.bi-calendar-day::before { content: "\f1e6"; }
.bi-calendar-event-fill::before { content: "\f1e7"; }
.bi-calendar-event::before { content: "\f1e8"; }
.bi-calendar-fill::before { content: "\f1e9"; }
.bi-calendar-minus-fill::before { content: "\f1ea"; }
.bi-calendar-minus::before { content: "\f1eb"; }
.bi-calendar-month-fill::before { content: "\f1ec"; }
.bi-calendar-month::before { content: "\f1ed"; }
.bi-calendar-plus-fill::before { content: "\f1ee"; }
.bi-calendar-plus::before { content: "\f1ef"; }
.bi-calendar-range-fill::before { content: "\f1f0"; }
.bi-calendar-range::before { content: "\f1f1"; }
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.bi-building-exclamation::before { content: "\f86b"; }
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.bi-scooter::before { content: "\f8ab"; }
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.bi-amd::before { content: "\f8ae"; }
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.bi-database-fill::before { content: "\f8be"; }
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.bi-database-up::before { content: "\f8c2"; }
.bi-database-x::before { content: "\f8c3"; }
.bi-database::before { content: "\f8c4"; }
.bi-houses-fill::before { content: "\f8c5"; }
.bi-houses::before { content: "\f8c6"; }
.bi-nvidia::before { content: "\f8c7"; }
.bi-person-vcard-fill::before { content: "\f8c8"; }
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<script>// mermaid-init.js
// Initializes the quarto-mermaid JS runtime
//
// Copyright (C) 2022 Posit Software, PBC
/**
* String.prototype.replaceAll() polyfill
* https://gomakethings.com/how-to-replace-a-section-of-a-string-with-another-one-with-vanilla-js/
* @author Chris Ferdinandi
* @license MIT
*/
if (!String.prototype.replaceAll) {
String.prototype.replaceAll = function (str, newStr) {
// If a regex pattern
if (
Object.prototype.toString.call(str).toLowerCase() === "[object regexp]"
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return this.replace(str, newStr);
}
// If a string
return this.replace(new RegExp(str, "g"), newStr);
};
}
const mermaidOpts = {
startOnLoad: false,
flowchart: {
htmlLabels: "false",
},
};
// this CSS is adapted from
// mkdocs-material
// Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>
const defaultCSS =
'.label text {fill: var(--mermaid-fg-color);}.node circle, .node ellipse, .node path, .node polygon, .node rect {fill: var(--mermaid-node-bg-color);stroke: var(--mermaid-node-fg-color);}marker {fill: var(--mermaid-edge-color) !important;}.edgeLabel .label rect {fill: #0000;}.label {color: var(--mermaid-label-fg-color);font-family: var(--mermaid-font-family);}.label foreignObject {line-height: normal;overflow: visible;}.label div .edgeLabel {color: var(--mermaid-label-fg-color);}.edgeLabel, .edgeLabel rect, .label div .edgeLabel {background-color: var(--mermaid-label-bg-color);}.edgeLabel, .edgeLabel rect {fill: var(--mermaid-label-bg-color);color: var(--mermaid-edge-color);}.edgePath .path, .flowchart-link {stroke: var(--mermaid-edge-color);}.edgePath .arrowheadPath {fill: var(--mermaid-edge-color);stroke: none;}.cluster rect {fill: var(--mermaid-fg-color--lightest);stroke: var(--mermaid-fg-color--lighter);}.cluster span {color: var(--mermaid-label-fg-color);font-family: var(--mermaid-font-family);}defs #flowchart-circleEnd, defs #flowchart-circleStart, defs #flowchart-crossEnd, defs #flowchart-crossStart, defs #flowchart-pointEnd, defs #flowchart-pointStart {stroke: none;}g.classGroup line, g.classGroup rect {fill: var(--mermaid-node-bg-color);stroke: var(--mermaid-node-fg-color);}g.classGroup text {fill: var(--mermaid-label-fg-color);font-family: var(--mermaid-font-family);}.classLabel .box {fill: var(--mermaid-label-bg-color);background-color: var(--mermaid-label-bg-color);opacity: 1;}.classLabel .label {fill: var(--mermaid-label-fg-color);font-family: var(--mermaid-font-family);}.node .divider {stroke: var(--mermaid-node-fg-color);}.relation {stroke: var(--mermaid-edge-color);}.cardinality {fill: var(--mermaid-label-fg-color);font-family: var(--mermaid-font-family);}.cardinality text {fill: inherit !important;}defs #classDiagram-compositionEnd, defs #classDiagram-compositionStart, defs #classDiagram-dependencyEnd, defs #classDiagram-dependencyStart, defs #classDiagram-extensionEnd, defs #classDiagram-extensionStart {fill: var(--mermaid-edge-color) !important;stroke: var(--mermaid-edge-color) !important;}defs #classDiagram-aggregationEnd, defs #classDiagram-aggregationStart {fill: var(--mermaid-label-bg-color) !important;stroke: var(--mermaid-edge-color) !important;}g.stateGroup rect {fill: var(--mermaid-node-bg-color);stroke: var(--mermaid-node-fg-color);}g.stateGroup .state-title {fill: var(--mermaid-label-fg-color) !important;font-family: var(--mermaid-font-family);}g.stateGroup .composit {fill: var(--mermaid-label-bg-color);}.nodeLabel {color: var(--mermaid-label-fg-color);font-family: var(--mermaid-font-family);}.node circle.state-end, .node circle.state-start, .start-state {fill: var(--mermaid-edge-color);stroke: none;}.end-state-inner, .end-state-outer {fill: var(--mermaid-edge-color);}.end-state-inner, .node circle.state-end {stroke: var(--mermaid-label-bg-color);}.transition {stroke: var(--mermaid-edge-color);}[id^="state-fork"] rect, [id^="state-join"] rect {fill: var(--mermaid-edge-color) !important;stroke: none !important;}.statediagram-cluster.statediagram-cluster .inner {fill: var(--mermaid-bg-color);}.statediagram-cluster rect {fill: var(--mermaid-node-bg-color);stroke: var(--mermaid-node-fg-color);}.statediagram-state rect.divider {fill: var(--mermaid-fg-color--lightest);stroke: var(--mermaid-fg-color--lighter);}defs #statediagram-barbEnd {stroke: var(--mermaid-edge-color);}.entityBox {fill: var(--mermaid-label-bg-color);stroke: var(--mermaid-node-fg-color);}.entityLabel {fill: var(--mermaid-label-fg-color);font-family: var(--mermaid-font-family);}.relationshipLabelBox {fill: var(--mermaid-label-bg-color);fill-opacity: 1;background-color: var(--mermaid-label-bg-color);opacity: 1;}.relationshipLabel {fill: var(--mermaid-label-fg-color);}.relationshipLine {stroke: var(--mermaid-edge-color);}defs #ONE_OR_MORE_END *, defs #ONE_OR_MORE_START *, defs #ONLY_ONE_END *, defs #ONLY_ONE_START *, defs #ZERO_OR_MORE_END *, defs #ZERO_OR_MORE_START *, defs #ZERO_OR_ONE_END *, defs #ZERO_OR_ONE_START * {stroke: va
const mermaidThemeEl = document.querySelector('meta[name="mermaid-theme"]');
if (mermaidThemeEl) {
mermaidOpts.theme = mermaidThemeEl.content;
} else {
mermaidOpts.themeCSS = defaultCSS;
}
mermaid.initialize(mermaidOpts);
const _quartoMermaid = {
// NB: there's effectively a copy of this function
// in `core/svg.ts`.
// if you change something here, you must keep it consistent there as well.
setSvgSize(svg) {
const { widthInPoints, heightInPoints, explicitHeight, explicitWidth } =
this.resolveSize(svg);
if (explicitWidth && explicitHeight) {
svg.setAttribute("width", widthInPoints);
svg.setAttribute("height", heightInPoints);
svg.style.maxWidth = null; // remove mermaid's default max-width
} else {
if (explicitWidth) {
svg.style.maxWidth = `${widthInPoints}px`;
}
if (explicitHeight) {
svg.style.maxHeight = `${heightInPoints}px`;
}
}
},
// NB: there's effectively a copy of this function
// in `core/svg.ts`.
// if you change something here, you must keep it consistent there as well.
makeResponsive(svg) {
const width = svg.getAttribute("width");
if (width === null) {
throw new Error("Couldn't find SVG width");
}
const numWidth = Number(width.slice(0, -2));
if (numWidth > 650) {
changed = true;
svg.setAttribute("width", "100%");
svg.removeAttribute("height");
}
},
// NB: there's effectively a copy of this function
// in `core/svg.ts`.
// if you change something here, you must keep it consistent there as well.
fixupAlignment(svg, align) {
let style = svg.getAttribute("style") || "";
switch (align) {
case "left":
style = `${style}; display: block; margin: auto auto auto 0`;
break;
case "right":
style = `${style}; display: block; margin: auto 0 auto auto`;
break;
case "center":
style = `${style}; display: block; margin: auto auto auto auto`;
break;
}
svg.setAttribute("style", style);
},
resolveOptions(svgEl) {
return svgEl.parentElement.parentElement.parentElement.parentElement
.dataset;
},
// NB: there's effectively a copy of this function
// in our mermaid runtime in `core/svg.ts`.
// if you change something here, you must keep it consistent there as well.
resolveSize(svgEl) {
const inInches = (size) => {
if (size.endsWith("in")) {
return Number(size.slice(0, -2));
}
if (size.endsWith("pt") || size.endsWith("px")) {
// assume 96 dpi for now
return Number(size.slice(0, -2)) / 96;
}
return Number(size);
};
// these are figWidth and figHeight on purpose,
// because data attributes are translated to camelCase by the DOM API
const kFigWidth = "figWidth",
kFigHeight = "figHeight";
const options = this.resolveOptions(svgEl);
const width = svgEl.getAttribute("width");
const height = svgEl.getAttribute("height");
if (!width || !height) {
// attempt to resolve figure dimensions via viewBox
throw new Error("Internal error: couldn't find figure dimensions");
}
const getViewBox = () => {
const vb = svgEl.attributes.getNamedItem("viewBox").value; // do it the roundabout way so that viewBox isn't dropped by deno_dom and text/html
if (!vb) return undefined;
const lst = vb.trim().split(" ").map(Number);
if (lst.length !== 4) return undefined;
if (lst.some(isNaN)) return undefined;
return lst;
};
let svgWidthInInches, svgHeightInInches;
if (
(width.slice(0, -2) === "pt" && height.slice(0, -2) === "pt") ||
(width.slice(0, -2) === "px" && height.slice(0, -2) === "px") ||
(!isNaN(Number(width)) && !isNaN(Number(height)))
) {
// we assume 96 dpi which is generally what seems to be used.
svgWidthInInches = Number(width.slice(0, -2)) / 96;
svgHeightInInches = Number(height.slice(0, -2)) / 96;
}
const viewBox = getViewBox();
if (viewBox !== undefined) {
// assume width and height come from viewbox.
const [_mx, _my, vbWidth, vbHeight] = viewBox;
svgWidthInInches = vbWidth / 96;
svgHeightInInches = vbHeight / 96;
} else {
throw new Error(
"Internal Error: Couldn't resolve width and height of SVG"
);
}
const svgWidthOverHeight = svgWidthInInches / svgHeightInInches;
let widthInInches, heightInInches;
if (options[kFigWidth] && options[kFigHeight]) {
// both were prescribed, so just go with them
widthInInches = inInches(String(options[kFigWidth]));
heightInInches = inInches(String(options[kFigHeight]));
} else if (options[kFigWidth]) {
// we were only given width, use that and adjust height based on aspect ratio;
widthInInches = inInches(String(options[kFigWidth]));
heightInInches = widthInInches / svgWidthOverHeight;
} else if (options[kFigHeight]) {
// we were only given height, use that and adjust width based on aspect ratio;
heightInInches = inInches(String(options[kFigHeight]));
widthInInches = heightInInches * svgWidthOverHeight;
} else {
// we were not given either, use svg's prescribed height
heightInInches = svgHeightInInches;
widthInInches = svgWidthInInches;
}
return {
widthInInches,
heightInInches,
widthInPoints: Math.round(widthInInches * 96),
heightInPoints: Math.round(heightInInches * 96),
explicitWidth: options?.[kFigWidth] !== undefined,
explicitHeight: options?.[kFigHeight] !== undefined,
};
},
postProcess(svg) {
const options = this.resolveOptions(svg);
if (
options.responsive &&
options["figWidth"] === undefined &&
options["figHeight"] === undefined
) {
this.makeResponsive(svg);
} else {
this.setSvgSize(svg);
}
if (options["reveal"]) {
this.fixupAlignment(svg, options["figAlign"] || "center");
}
// forward align attributes to the correct parent dif
// so that the svg figure is aligned correctly
const div = svg.parentElement.parentElement.parentElement;
const align = div.parentElement.parentElement.dataset.layoutAlign;
if (align) {
div.classList.remove("quarto-figure-left");
div.classList.remove("quarto-figure-center");
div.classList.remove("quarto-figure-right");
div.classList.add(`quarto-figure-${align}`);
}
},
};
// deno-lint-ignore no-window-prefix
window.addEventListener(
"load",
function () {
let i = 0;
// we need pre because of whitespace preservation
for (const el of Array.from(document.querySelectorAll("pre.mermaid-js"))) {
// &nbsp; doesn't appear to be treated as whitespace by mermaid
// so we replace it with a space.
const text = el.innerText.replaceAll("&nbsp;", " ");
const output = mermaid.mermaidAPI.render(
`mermaid-${++i}`,
text,
() => {},
el
);
el.innerHTML = output;
if (el.dataset.label) {
// patch mermaid's emitted style
const svg = el.firstChild;
const style = svg.querySelector("style");
style.innerHTML = style.innerHTML.replaceAll(
`#${svg.id}`,
`#${el.dataset.label}`
);
svg.id = el.dataset.label;
delete el.dataset.label;
}
const svg = el.querySelector("svg");
const parent = el.parentElement;
parent.removeChild(el);
parent.appendChild(svg);
svg.classList.add("mermaid-js");
}
for (const svgEl of Array.from(
document.querySelectorAll("svg.mermaid-js")
)) {
_quartoMermaid.postProcess(svgEl);
}
},
false
);
</script>
<style type="text/css">.mermaidTooltip {
position: absolute;
text-align: center;
max-width: 200px;
padding: 2px;
font-family: "trebuchet ms", verdana, arial;
font-size: 12px;
background: #ffffde;
border: 1px solid #aaaa33;
border-radius: 2px;
pointer-events: none;
z-index: 1000;
}
</style>
</head>
<body>
<div id="quarto-content" class="page-columns page-rows-contents page-layout-article">
<div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
<nav id="TOC" role="doc-toc" class="toc-active">
<h2 id="toc-title">Table of contents</h2>
<ul>
<li><a href="#the-data-sample" id="toc-the-data-sample" class="nav-link active" data-scroll-target="#the-data-sample">The data sample</a></li>
<li><a href="#preliminary-findings" id="toc-preliminary-findings" class="nav-link" data-scroll-target="#preliminary-findings">Preliminary findings</a></li>
<li><a href="#preliminary-limitations" id="toc-preliminary-limitations" class="nav-link" data-scroll-target="#preliminary-limitations">Preliminary limitations</a></li>
</ul>
<div class="quarto-alternate-formats"><h2>Other Formats</h2><ul><li><a href="meeting_eoy.docx"><i class="bi bi-file-word"></i>MS Word</a></li><li><a href="meeting_eoy.pdf"><i class="bi bi-file-pdf"></i>PDF</a></li></ul></div></nav>
</div>
<main class="content page-columns page-full" id="quarto-document-content">
<header id="title-block-header" class="quarto-title-block default">
<div class="quarto-title">
<div class="quarto-title-block"><div><h1 class="title">Scoping Review: Preliminary findings</h1><button type="button" class="btn code-tools-button" id="quarto-code-tools-source"><i class="bi"></i> Code</button></div></div>
<p class="subtitle lead">Addressing inequalities in the World of Work</p>
</div>
<div class="quarto-title-meta">
</div>
</header>
<section id="the-data-sample" class="level1">
<h1>The data sample</h1>
<div class="cell" data-fig-width="6">
<div class="cell-output-display">
<div id="fig-prisma" class="quarto-figure quarto-figure-center anchored">
<figure class="figure">
<div>
<pre class="mermaid mermaid-js" data-label="fig-prisma">flowchart TD;
search_db[&quot;Records identified through database searching (n=1643)&quot;] --&gt; starting_sample;
search_prev[&quot;Records identified through other sources (n=753)&quot;] --&gt; starting_sample[&quot;Starting sample (n=2396)&quot;];
starting_sample -- &quot;Duplicate removal (-22 removed) &quot;--&gt; dedup[&quot;Records after duplicates removed (n=2418)&quot;];
dedup -- &quot;Title screening (982 excluded)&quot; --&gt; title_screened[&quot;Records after titles screened (n=1436)&quot;];
title_screened -- &quot;Abstract screening (1039 excluded)&quot;--&gt; abstract_screened[&quot;Records after abstracts screened (n=397)&quot;];
abstract_screened -- &quot; Language screening (1 excluded) &quot;--&gt; language_screened[&quot;Records after language screened (n=396)&quot;];
language_screened -- &quot; Full-text screening (316 excluded) &quot;--&gt; full-text_screened[&quot;Full-text articles assessed for eligibility (n=38) STILL OUTSTANDING: 358&quot;];
</pre>
</div>
<figcaption class="figure-caption">Figure 1: Sample sorting process through identification and screening</figcaption>
</figure>
</div>
</div>
</div>
<ul>
<li>strongest focus on income inequality (vertical), with many horizontal inequality studies including aspect of income inequality</li>
<li>horizontal inequalities: strongest focus on income - gender inequalities (horizontal)</li>
<li>interventions:
<ul>
<li>strongest research base on labour rights protection interventions</li>
<li>second on infrastructural interventions</li>
<li>third on agency-strengthening ones: training, financial access, education programmes</li>
</ul></li>
<li>formalization &amp; social protection research rarely goes into inequality outcomes beyond income effects; most excluded for that reason</li>
</ul>
<div class="cell" data-execution_count="3">
<div class="cell-output cell-output-display">
<div id="fig-inequality-types-whole-sample" class="quarto-figure quarto-figure-center anchored">
<figure class="figure">
<p><img src="data:image/svg+xml;base64,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
<figcaption class="figure-caption">Figure 2: Overall inequality types in sample</figcaption>
</figure>
</div>
</div>
</div>
</section>
<section id="preliminary-findings" class="level1">
<h1>Preliminary findings</h1>
<div class="cell" data-execution_count="4">
<div class="cell-output cell-output-display">
<div id="fig-inequality-types" class="quarto-figure quarto-figure-center anchored">
<figure class="figure">
<p><img src="data:image/svg+xml;base64,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
<figcaption class="figure-caption">Figure 3: Finished and projected inequality types</figcaption>
</figure>
</div>
</div>
</div>
<ul>
<li>interventions most strongly target gender-income divide
<ul>
<li>most studies here recommend further scale-integration between agency/structural approaches</li>
<li>most studies also only focus on analysing a single scale however</li>
</ul></li>
<li>interventions often have intersectional impacts even if not targeted at them
<ul>
<li>most visible for institutional/structural interventions and spatial inequalities</li>
<li>studies analysing intersectional inequalities near unanimously recommend intersectional targeting</li>
</ul></li>
<li>individual agency-based interventions (training, subsidies, maternity benefits, transfers, microcredit, etc):
<ul>
<li>seem most effective for targeting WoW outcomes of disability inequalities</li>
<li>seem marginally effective for targeting WoW outcomes of gender inequalities</li>
<li>require additional mediating scales for other inequalities</li>
</ul></li>
<li>more structural interventions (education, infrastructural, ubi, trade liberalization, collective action):
<ul>
<li>seem most effective for spatial, income, education-generational inequalities</li>
<li>often show longer-term impacts, requiring longer periods of analyses</li>
<li>can work without additional agency-based interventions, few studies analyse both at same time</li>
</ul></li>
</ul>
</section>
<section id="preliminary-limitations" class="level1 page-columns page-full">
<h1>Preliminary limitations</h1>
<div class="cell" data-execution_count="5">
<div class="cell-output cell-output-display">
<div id="fig-intervention-types" class="quarto-figure quarto-figure-center anchored">
<figure class="figure">
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<figcaption class="figure-caption">Figure 4: Finished and projected intervention types</figcaption>
</figure>
</div>
</div>
</div>
<ul>
<li>stronger institutional-structural research focus in developed countries, with more structural-agency based in developing countries</li>
<li>employment creation as a category is often subsumed in other structural/institutional analyses</li>
<li>little evidence-based research on effect of interventions targeting education on world of work outcomes</li>
<li>spatial inequality most evenly geographically spread evidence base</li>
<li>empirical base on interventions targeting disability inequalities strongly restricted on developed countries, especially United States</li>
</ul>
<div class="cell page-columns page-full" data-execution_count="6">
<div class="cell-output cell-output-display column-screen">
<div id="fig-countries" class="quarto-figure quarto-figure-center anchored">
<figure class="figure">
<p><img src="data:image/svg+xml;base64,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
<figcaption class="figure-caption">Figure 5: Country spread</figcaption>
</figure>
</div>
</div>
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</script><div class="modal fade" id="quarto-embedded-source-code-modal" tabindex="-1" aria-labelledby="quarto-embedded-source-code-modal-label" aria-hidden="true"><div class="modal-dialog modal-dialog-scrollable"><div class="modal-content"><div class="modal-header"><h5 class="modal-title" id="quarto-embedded-source-code-modal-label">Source Code</h5><button class="btn-close" data-bs-dismiss="modal"></button></div><div class="modal-body"><div class>
<div class="sourceCode" id="cb1" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="an">bibliography:</span><span class="co"> data/intermediate/zotero-library.bib</span></span>
2024-01-06 09:41:26 +00:00
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="an">csl:</span><span class="co"> /home/marty/documents/library/utilities/styles/APA-7.csl</span></span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="an">papersize:</span><span class="co"> A4</span></span>
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="an">linestretch:</span><span class="co"> 1.5</span></span>
<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="an">fontfamily:</span><span class="co"> lmodern</span></span>
<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="an">fontsize:</span><span class="co"> &quot;12&quot;</span></span>
<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a><span class="an">geometry:</span></span>
<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="co"> - left=2.2cm</span></span>
<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a><span class="co"> - right=3.5cm</span></span>
<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a><span class="co"> - top=2.5cm</span></span>
<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a><span class="co"> - bottom=2.5cm</span></span>
<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a><span class="an">lang:</span><span class="co"> en</span></span>
<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> &quot;Scoping Review: Preliminary findings&quot;</span></span>
<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a><span class="an">subtitle:</span><span class="co"> Addressing inequalities in the World of Work</span></span>
<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-20"><a href="#cb1-20" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb1-21"><a href="#cb1-21" aria-hidden="true" tabindex="-1"></a><span class="co">#| echo: false</span></span>
<span id="cb1-22"><a href="#cb1-22" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> pathlib <span class="im">import</span> Path</span>
<span id="cb1-23"><a href="#cb1-23" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> re</span>
<span id="cb1-24"><a href="#cb1-24" aria-hidden="true" tabindex="-1"></a><span class="co">## standard imports</span></span>
<span id="cb1-25"><a href="#cb1-25" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> IPython.core.display <span class="im">import</span> Markdown <span class="im">as</span> md</span>
<span id="cb1-26"><a href="#cb1-26" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
<span id="cb1-27"><a href="#cb1-27" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
<span id="cb1-28"><a href="#cb1-28" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> matplotlib <span class="im">import</span> pyplot <span class="im">as</span> plt</span>
<span id="cb1-29"><a href="#cb1-29" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> seaborn <span class="im">as</span> sns</span>
<span id="cb1-30"><a href="#cb1-30" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tabulate <span class="im">import</span> tabulate</span>
<span id="cb1-31"><a href="#cb1-31" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> bibtexparser</span>
<span id="cb1-32"><a href="#cb1-32" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-33"><a href="#cb1-33" aria-hidden="true" tabindex="-1"></a>sns.set_style(<span class="st">&quot;whitegrid&quot;</span>)</span>
<span id="cb1-34"><a href="#cb1-34" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-35"><a href="#cb1-35" aria-hidden="true" tabindex="-1"></a>DATA_DIR<span class="op">=</span>Path(<span class="st">&quot;./data&quot;</span>)</span>
2024-01-06 09:41:26 +00:00
<span id="cb1-36"><a href="#cb1-36" aria-hidden="true" tabindex="-1"></a>RAW_DATA<span class="op">=</span>DATA_DIR.joinpath(<span class="st">&quot;raw&quot;</span>)</span>
<span id="cb1-37"><a href="#cb1-37" aria-hidden="true" tabindex="-1"></a>WORKING_DATA<span class="op">=</span>DATA_DIR.joinpath(<span class="st">&quot;intermediate&quot;</span>)</span>
<span id="cb1-38"><a href="#cb1-38" aria-hidden="true" tabindex="-1"></a>PROCESSED_DATA<span class="op">=</span>DATA_DIR.joinpath(<span class="st">&quot;processed&quot;</span>)</span>
<span id="cb1-39"><a href="#cb1-39" aria-hidden="true" tabindex="-1"></a>SUPPLEMENTARY_DATA<span class="op">=</span>DATA_DIR.joinpath(<span class="st">&quot;supplementary&quot;</span>)</span>
<span id="cb1-40"><a href="#cb1-40" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-41"><a href="#cb1-41" aria-hidden="true" tabindex="-1"></a>bib_string<span class="op">=</span><span class="st">&quot;&quot;</span></span>
<span id="cb1-42"><a href="#cb1-42" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> partial_bib <span class="kw">in</span> RAW_DATA.glob(<span class="st">&quot;**/*.bib&quot;</span>):</span>
<span id="cb1-43"><a href="#cb1-43" aria-hidden="true" tabindex="-1"></a> <span class="cf">with</span> <span class="bu">open</span>(partial_bib) <span class="im">as</span> f:</span>
<span id="cb1-44"><a href="#cb1-44" aria-hidden="true" tabindex="-1"></a> bib_string<span class="op">+=</span><span class="st">&quot;</span><span class="ch">\n</span><span class="st">&quot;</span>.join(f.readlines())</span>
<span id="cb1-45"><a href="#cb1-45" aria-hidden="true" tabindex="-1"></a>bib_sample_raw_db <span class="op">=</span> bibtexparser.parse_string(bib_string)</span>
<span id="cb1-46"><a href="#cb1-46" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-47"><a href="#cb1-47" aria-hidden="true" tabindex="-1"></a>bib_string<span class="op">=</span><span class="st">&quot;&quot;</span></span>
<span id="cb1-48"><a href="#cb1-48" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> partial_bib <span class="kw">in</span> WORKING_DATA.glob(<span class="st">&quot;**/*.bib&quot;</span>):</span>
<span id="cb1-49"><a href="#cb1-49" aria-hidden="true" tabindex="-1"></a> <span class="cf">with</span> <span class="bu">open</span>(partial_bib) <span class="im">as</span> f:</span>
<span id="cb1-50"><a href="#cb1-50" aria-hidden="true" tabindex="-1"></a> bib_string<span class="op">+=</span><span class="st">&quot;</span><span class="ch">\n</span><span class="st">&quot;</span>.join(f.readlines())</span>
<span id="cb1-51"><a href="#cb1-51" aria-hidden="true" tabindex="-1"></a>bib_sample <span class="op">=</span> bibtexparser.parse_string(bib_string)</span>
<span id="cb1-52"><a href="#cb1-52" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-53"><a href="#cb1-53" aria-hidden="true" tabindex="-1"></a><span class="co"># load relevant studies</span></span>
<span id="cb1-54"><a href="#cb1-54" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> src <span class="im">import</span> data</span>
<span id="cb1-55"><a href="#cb1-55" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-56"><a href="#cb1-56" aria-hidden="true" tabindex="-1"></a><span class="co"># load zotero-based metadata: citations and uses</span></span>
<span id="cb1-57"><a href="#cb1-57" aria-hidden="true" tabindex="-1"></a>zot_df <span class="op">=</span> pd.DataFrame([</span>
<span id="cb1-58"><a href="#cb1-58" aria-hidden="true" tabindex="-1"></a> [</span>
<span id="cb1-59"><a href="#cb1-59" aria-hidden="true" tabindex="-1"></a> entry[<span class="st">&quot;doi&quot;</span>] <span class="cf">if</span> <span class="st">&quot;doi&quot;</span> <span class="kw">in</span> entry.fields_dict <span class="cf">else</span> <span class="va">None</span>,</span>
<span id="cb1-60"><a href="#cb1-60" aria-hidden="true" tabindex="-1"></a> entry[<span class="st">&quot;times-cited&quot;</span>] <span class="cf">if</span> <span class="st">&quot;times-cited&quot;</span> <span class="kw">in</span> entry.fields_dict <span class="cf">else</span> <span class="va">None</span>,</span>
<span id="cb1-61"><a href="#cb1-61" aria-hidden="true" tabindex="-1"></a> entry[<span class="st">&quot;usage&quot;</span>] <span class="cf">if</span> <span class="st">&quot;usage&quot;</span> <span class="kw">in</span> entry.fields_dict <span class="cf">else</span> <span class="va">None</span>,</span>
<span id="cb1-62"><a href="#cb1-62" aria-hidden="true" tabindex="-1"></a> entry[<span class="st">&quot;keywords&quot;</span>] <span class="cf">if</span> <span class="st">&quot;keywords&quot;</span> <span class="kw">in</span> entry.fields_dict <span class="cf">else</span> <span class="va">None</span>,</span>
<span id="cb1-63"><a href="#cb1-63" aria-hidden="true" tabindex="-1"></a> ]</span>
<span id="cb1-64"><a href="#cb1-64" aria-hidden="true" tabindex="-1"></a> <span class="cf">for</span> entry <span class="kw">in</span> bib_sample.entries</span>
<span id="cb1-65"><a href="#cb1-65" aria-hidden="true" tabindex="-1"></a>], columns <span class="op">=</span> [<span class="st">&quot;doi&quot;</span>, <span class="st">&quot;cited&quot;</span>, <span class="st">&quot;usage&quot;</span>, <span class="st">&quot;keywords&quot;</span>]).drop_duplicates(<span class="st">&quot;doi&quot;</span>).set_index(<span class="st">&quot;doi&quot;</span>)</span>
<span id="cb1-66"><a href="#cb1-66" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-67"><a href="#cb1-67" aria-hidden="true" tabindex="-1"></a><span class="co"># Add WB country grouping definitions (income group, world region)</span></span>
<span id="cb1-68"><a href="#cb1-68" aria-hidden="true" tabindex="-1"></a>WB_COUNTRY_GROUPS_FILE <span class="op">=</span> Path(<span class="ss">f&quot;</span><span class="sc">{</span>SUPPLEMENTARY_DATA<span class="sc">}</span><span class="ss">/wb-country-groupings.xlsx&quot;</span>).resolve()</span>
<span id="cb1-69"><a href="#cb1-69" aria-hidden="true" tabindex="-1"></a>df_country_groups <span class="op">=</span> pd.read_excel(WB_COUNTRY_GROUPS_FILE).set_index(<span class="st">&quot;Economy&quot;</span>)</span>
<span id="cb1-70"><a href="#cb1-70" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-71"><a href="#cb1-71" aria-hidden="true" tabindex="-1"></a>bib_df <span class="op">=</span> (data.from_yml(<span class="ss">f&quot;</span><span class="sc">{</span>PROCESSED_DATA<span class="sc">}</span><span class="ss">/relevant&quot;</span>)</span>
<span id="cb1-72"><a href="#cb1-72" aria-hidden="true" tabindex="-1"></a> .assign(</span>
<span id="cb1-73"><a href="#cb1-73" aria-hidden="true" tabindex="-1"></a> doi<span class="op">=</span><span class="kw">lambda</span> _df: _df[<span class="st">&quot;uri&quot;</span>].<span class="bu">str</span>.extract(<span class="vs">r&quot;https?://(?:dx\.)?doi\.org/(.*)&quot;</span>, expand<span class="op">=</span><span class="va">False</span>),</span>
<span id="cb1-74"><a href="#cb1-74" aria-hidden="true" tabindex="-1"></a> zot_cited<span class="op">=</span><span class="kw">lambda</span> _df: _df[<span class="st">&quot;doi&quot;</span>].<span class="bu">map</span>(zot_df[<span class="st">&quot;cited&quot;</span>]),</span>
<span id="cb1-75"><a href="#cb1-75" aria-hidden="true" tabindex="-1"></a> zot_usage<span class="op">=</span><span class="kw">lambda</span> _df: _df[<span class="st">&quot;doi&quot;</span>].<span class="bu">map</span>(zot_df[<span class="st">&quot;usage&quot;</span>]),</span>
<span id="cb1-76"><a href="#cb1-76" aria-hidden="true" tabindex="-1"></a> zot_keywords<span class="op">=</span><span class="kw">lambda</span> _df: _df[<span class="st">&quot;doi&quot;</span>].<span class="bu">map</span>(zot_df[<span class="st">&quot;keywords&quot;</span>]),</span>
<span id="cb1-77"><a href="#cb1-77" aria-hidden="true" tabindex="-1"></a> date <span class="op">=</span> <span class="kw">lambda</span> _df: pd.to_datetime(_df[<span class="st">&quot;year&quot;</span>], <span class="bu">format</span><span class="op">=</span><span class="st">&quot;%Y&quot;</span>),</span>
<span id="cb1-78"><a href="#cb1-78" aria-hidden="true" tabindex="-1"></a> year <span class="op">=</span> <span class="kw">lambda</span> _df: _df[<span class="st">&quot;date&quot;</span>].dt.year,</span>
<span id="cb1-79"><a href="#cb1-79" aria-hidden="true" tabindex="-1"></a> region <span class="op">=</span> <span class="kw">lambda</span> _df: _df[<span class="st">&quot;country&quot;</span>].<span class="bu">map</span>(df_country_groups[<span class="st">&quot;Region&quot;</span>]),</span>
<span id="cb1-80"><a href="#cb1-80" aria-hidden="true" tabindex="-1"></a> income_group <span class="op">=</span> <span class="kw">lambda</span> _df: _df[<span class="st">&quot;country&quot;</span>].<span class="bu">map</span>(df_country_groups[<span class="st">&quot;Income group&quot;</span>]),</span>
<span id="cb1-81"><a href="#cb1-81" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb1-82"><a href="#cb1-82" aria-hidden="true" tabindex="-1"></a> .query(<span class="st">&quot;year &gt;= 2000&quot;</span>)</span>
<span id="cb1-83"><a href="#cb1-83" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb1-84"><a href="#cb1-84" aria-hidden="true" tabindex="-1"></a>zot_df <span class="op">=</span> <span class="va">None</span></span>
<span id="cb1-85"><a href="#cb1-85" aria-hidden="true" tabindex="-1"></a>df_country_groups <span class="op">=</span> <span class="va">None</span></span>
<span id="cb1-86"><a href="#cb1-86" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb1-87"><a href="#cb1-87" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-88"><a href="#cb1-88" aria-hidden="true" tabindex="-1"></a><span class="fu"># The data sample</span></span>
<span id="cb1-89"><a href="#cb1-89" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-92"><a href="#cb1-92" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb1-93"><a href="#cb1-93" aria-hidden="true" tabindex="-1"></a><span class="co">#| echo: false</span></span>
<span id="cb1-94"><a href="#cb1-94" aria-hidden="true" tabindex="-1"></a><span class="co">#| output: asis</span></span>
<span id="cb1-95"><a href="#cb1-95" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-96"><a href="#cb1-96" aria-hidden="true" tabindex="-1"></a>FULL_RAW_SAMPLE_NOTHING_REMOVED <span class="op">=</span> <span class="dv">2396</span></span>
<span id="cb1-97"><a href="#cb1-97" aria-hidden="true" tabindex="-1"></a>nr_database_query_raw <span class="op">=</span> <span class="bu">len</span>(bib_sample_raw_db.entries)</span>
<span id="cb1-98"><a href="#cb1-98" aria-hidden="true" tabindex="-1"></a>nr_out_duplicates <span class="op">=</span> FULL_RAW_SAMPLE_NOTHING_REMOVED <span class="op">-</span> <span class="bu">len</span>(bib_sample.entries)</span>
<span id="cb1-99"><a href="#cb1-99" aria-hidden="true" tabindex="-1"></a>nr_other_sources <span class="op">=</span> (<span class="bu">len</span>(bib_sample.entries) <span class="op">+</span> nr_out_duplicates) <span class="op">-</span> nr_database_query_raw</span>
<span id="cb1-100"><a href="#cb1-100" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-101"><a href="#cb1-101" aria-hidden="true" tabindex="-1"></a>all_keywords <span class="op">=</span> [entry[<span class="st">&quot;keywords&quot;</span>] <span class="cf">for</span> entry <span class="kw">in</span> bib_sample.entries <span class="cf">if</span> <span class="st">&quot;keywords&quot;</span> <span class="kw">in</span> entry.fields_dict.keys()]</span>
<span id="cb1-102"><a href="#cb1-102" aria-hidden="true" tabindex="-1"></a>nr_out_title <span class="op">=</span> <span class="bu">len</span>([<span class="dv">1</span> <span class="cf">for</span> kw <span class="kw">in</span> all_keywords <span class="cf">if</span> <span class="st">&quot;out::title&quot;</span> <span class="kw">in</span> kw]) <span class="op">+</span> <span class="dv">400</span></span>
<span id="cb1-103"><a href="#cb1-103" aria-hidden="true" tabindex="-1"></a>nr_out_abstract <span class="op">=</span> <span class="bu">len</span>([<span class="dv">1</span> <span class="cf">for</span> kw <span class="kw">in</span> all_keywords <span class="cf">if</span> <span class="st">&quot;out::abstract&quot;</span> <span class="kw">in</span> kw]) <span class="op">+</span> <span class="dv">400</span></span>
<span id="cb1-104"><a href="#cb1-104" aria-hidden="true" tabindex="-1"></a>nr_out_fulltext <span class="op">=</span> <span class="bu">len</span>([<span class="dv">1</span> <span class="cf">for</span> kw <span class="kw">in</span> all_keywords <span class="cf">if</span> <span class="st">&quot;out::full-text&quot;</span> <span class="kw">in</span> kw]) <span class="op">+</span> <span class="dv">300</span></span>
<span id="cb1-105"><a href="#cb1-105" aria-hidden="true" tabindex="-1"></a>nr_out_language <span class="op">=</span> <span class="bu">len</span>([<span class="dv">1</span> <span class="cf">for</span> kw <span class="kw">in</span> all_keywords <span class="cf">if</span> <span class="st">&quot;out::language&quot;</span> <span class="kw">in</span> kw])</span>
<span id="cb1-106"><a href="#cb1-106" aria-hidden="true" tabindex="-1"></a>nr_extraction_done <span class="op">=</span> <span class="bu">len</span>([<span class="dv">1</span> <span class="cf">for</span> kw <span class="kw">in</span> all_keywords <span class="cf">if</span> <span class="st">&quot;done::extracted&quot;</span> <span class="kw">in</span> kw])</span>
<span id="cb1-107"><a href="#cb1-107" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-108"><a href="#cb1-108" aria-hidden="true" tabindex="-1"></a>t3 <span class="op">=</span> <span class="st">&quot;`&quot;</span> <span class="op">*</span> <span class="dv">3</span></span>
<span id="cb1-109"><a href="#cb1-109" aria-hidden="true" tabindex="-1"></a><span class="co"># </span><span class="al">FIXME</span><span class="co"> use data/supplementary undeduplciated counts to get database starting and snowballing counts</span></span>
2024-01-06 09:41:26 +00:00
<span id="cb1-110"><a href="#cb1-110" aria-hidden="true" tabindex="-1"></a><span class="co"># from: https://github.com/quarto-dev/quarto-cli/discussions/6508</span></span>
<span id="cb1-111"><a href="#cb1-111" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f&quot;&quot;&quot;</span></span>
<span id="cb1-112"><a href="#cb1-112" aria-hidden="true" tabindex="-1"></a><span class="ss">```</span><span class="sc">{</span>mermaid<span class="sc">}</span></span>
<span id="cb1-113"><a href="#cb1-113" aria-hidden="true" tabindex="-1"></a><span class="ss">%%| label: fig-prisma</span></span>
<span id="cb1-114"><a href="#cb1-114" aria-hidden="true" tabindex="-1"></a><span class="ss">%%| fig-cap: &quot;Sample sorting process through identification and screening&quot;</span></span>
<span id="cb1-115"><a href="#cb1-115" aria-hidden="true" tabindex="-1"></a><span class="ss">%%| fig-width: 6</span></span>
<span id="cb1-116"><a href="#cb1-116" aria-hidden="true" tabindex="-1"></a><span class="ss">flowchart TD;</span></span>
<span id="cb1-117"><a href="#cb1-117" aria-hidden="true" tabindex="-1"></a><span class="ss"> search_db[&quot;Records identified through database searching (n=</span><span class="sc">{</span>nr_database_query_raw<span class="sc">}</span><span class="ss">)&quot;] --&gt; starting_sample;</span></span>
<span id="cb1-118"><a href="#cb1-118" aria-hidden="true" tabindex="-1"></a><span class="ss"> search_prev[&quot;Records identified through other sources (n=</span><span class="sc">{</span>nr_other_sources<span class="sc">}</span><span class="ss">)&quot;] --&gt; starting_sample[&quot;Starting sample (n=</span><span class="sc">{</span>FULL_RAW_SAMPLE_NOTHING_REMOVED<span class="sc">}</span><span class="ss">)&quot;];</span></span>
<span id="cb1-119"><a href="#cb1-119" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-120"><a href="#cb1-120" aria-hidden="true" tabindex="-1"></a><span class="ss"> starting_sample -- &quot;Duplicate removal (</span><span class="sc">{</span>nr_out_duplicates<span class="sc">}</span><span class="ss"> removed) &quot;--&gt; dedup[&quot;Records after duplicates removed (n=</span><span class="sc">{</span><span class="bu">len</span>(bib_sample.entries)<span class="sc">}</span><span class="ss">)&quot;];</span></span>
<span id="cb1-121"><a href="#cb1-121" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-122"><a href="#cb1-122" aria-hidden="true" tabindex="-1"></a><span class="ss"> dedup -- &quot;Title screening (</span><span class="sc">{</span>nr_out_title<span class="sc">}</span><span class="ss"> excluded)&quot; --&gt; title_screened[&quot;Records after titles screened (n=</span><span class="sc">{</span><span class="bu">len</span>(bib_sample.entries) <span class="op">-</span> nr_out_title<span class="sc">}</span><span class="ss">)&quot;];</span></span>
<span id="cb1-123"><a href="#cb1-123" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-124"><a href="#cb1-124" aria-hidden="true" tabindex="-1"></a><span class="ss"> title_screened -- &quot;Abstract screening (</span><span class="sc">{</span>nr_out_abstract<span class="sc">}</span><span class="ss"> excluded)&quot;--&gt; abstract_screened[&quot;Records after abstracts screened (n=</span><span class="sc">{</span><span class="bu">len</span>(bib_sample.entries)<span class="op">-</span>nr_out_title<span class="op">-</span>nr_out_abstract<span class="sc">}</span><span class="ss">)&quot;];</span></span>
<span id="cb1-125"><a href="#cb1-125" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-126"><a href="#cb1-126" aria-hidden="true" tabindex="-1"></a><span class="ss"> abstract_screened -- &quot; Language screening (</span><span class="sc">{</span>nr_out_language<span class="sc">}</span><span class="ss"> excluded) &quot;--&gt; language_screened[&quot;Records after language screened (n=</span><span class="sc">{</span><span class="bu">len</span>(bib_sample.entries)<span class="op">-</span>nr_out_title<span class="op">-</span>nr_out_abstract<span class="op">-</span>nr_out_language<span class="sc">}</span><span class="ss">)&quot;];</span></span>
<span id="cb1-127"><a href="#cb1-127" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-128"><a href="#cb1-128" aria-hidden="true" tabindex="-1"></a><span class="ss"> language_screened -- &quot; Full-text screening (</span><span class="sc">{</span>nr_out_fulltext<span class="sc">}</span><span class="ss"> excluded) &quot;--&gt; full-text_screened[&quot;Full-text articles assessed for eligibility (n=</span><span class="sc">{</span>nr_extraction_done<span class="sc">}</span><span class="ss">) STILL OUTSTANDING: </span><span class="sc">{</span><span class="bu">len</span>(bib_sample.entries)<span class="op">-</span>nr_out_title<span class="op">-</span>nr_out_abstract<span class="op">-</span>nr_out_language <span class="op">-</span> nr_extraction_done<span class="sc">}</span><span class="ss">&quot;];</span></span>
<span id="cb1-129"><a href="#cb1-129" aria-hidden="true" tabindex="-1"></a><span class="sc">{</span>t3<span class="sc">}</span></span>
<span id="cb1-130"><a href="#cb1-130" aria-hidden="true" tabindex="-1"></a><span class="ss">&quot;&quot;&quot;</span>)</span>
<span id="cb1-131"><a href="#cb1-131" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb1-132"><a href="#cb1-132" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-133"><a href="#cb1-133" aria-hidden="true" tabindex="-1"></a><span class="ss">- </span>strongest focus on income inequality (vertical), with many horizontal inequality studies including aspect of income inequality</span>
<span id="cb1-134"><a href="#cb1-134" aria-hidden="true" tabindex="-1"></a><span class="ss">- </span>horizontal inequalities: strongest focus on income - gender inequalities (horizontal)</span>
<span id="cb1-135"><a href="#cb1-135" aria-hidden="true" tabindex="-1"></a><span class="ss">- </span>interventions:</span>
<span id="cb1-136"><a href="#cb1-136" aria-hidden="true" tabindex="-1"></a><span class="ss"> - </span>strongest research base on labour rights protection interventions</span>
<span id="cb1-137"><a href="#cb1-137" aria-hidden="true" tabindex="-1"></a><span class="ss"> - </span>second on infrastructural interventions</span>
<span id="cb1-138"><a href="#cb1-138" aria-hidden="true" tabindex="-1"></a><span class="ss"> - </span>third on agency-strengthening ones: training, financial access, education programmes</span>
<span id="cb1-139"><a href="#cb1-139" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-140"><a href="#cb1-140" aria-hidden="true" tabindex="-1"></a><span class="ss">- </span>formalization &amp; social protection research rarely goes into inequality outcomes beyond &#39;income&#39; effects; most excluded for that reason</span>
<span id="cb1-141"><a href="#cb1-141" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-144"><a href="#cb1-144" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb1-145"><a href="#cb1-145" aria-hidden="true" tabindex="-1"></a><span class="co">#| echo: false</span></span>
<span id="cb1-146"><a href="#cb1-146" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: fig-inequality-types-whole-sample</span></span>
<span id="cb1-147"><a href="#cb1-147" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-cap: Overall inequality types in sample</span></span>
<span id="cb1-148"><a href="#cb1-148" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-149"><a href="#cb1-149" aria-hidden="true" tabindex="-1"></a><span class="co"># load zotero-based metadata: citations and uses</span></span>
<span id="cb1-150"><a href="#cb1-150" aria-hidden="true" tabindex="-1"></a>pi <span class="op">=</span> (pd.DataFrame([</span>
<span id="cb1-151"><a href="#cb1-151" aria-hidden="true" tabindex="-1"></a> [</span>
<span id="cb1-152"><a href="#cb1-152" aria-hidden="true" tabindex="-1"></a> entry[<span class="st">&quot;doi&quot;</span>] <span class="cf">if</span> <span class="st">&quot;doi&quot;</span> <span class="kw">in</span> entry.fields_dict <span class="cf">else</span> <span class="va">None</span>,</span>
<span id="cb1-153"><a href="#cb1-153" aria-hidden="true" tabindex="-1"></a> entry[<span class="st">&quot;times-cited&quot;</span>] <span class="cf">if</span> <span class="st">&quot;times-cited&quot;</span> <span class="kw">in</span> entry.fields_dict <span class="cf">else</span> <span class="va">None</span>,</span>
<span id="cb1-154"><a href="#cb1-154" aria-hidden="true" tabindex="-1"></a> entry[<span class="st">&quot;usage&quot;</span>] <span class="cf">if</span> <span class="st">&quot;usage&quot;</span> <span class="kw">in</span> entry.fields_dict <span class="cf">else</span> <span class="va">None</span>,</span>
<span id="cb1-155"><a href="#cb1-155" aria-hidden="true" tabindex="-1"></a> entry[<span class="st">&quot;keywords&quot;</span>] <span class="cf">if</span> <span class="st">&quot;keywords&quot;</span> <span class="kw">in</span> entry.fields_dict <span class="cf">else</span> <span class="va">None</span>,</span>
<span id="cb1-156"><a href="#cb1-156" aria-hidden="true" tabindex="-1"></a> ]</span>
<span id="cb1-157"><a href="#cb1-157" aria-hidden="true" tabindex="-1"></a> <span class="cf">for</span> entry <span class="kw">in</span> bib_sample.entries</span>
<span id="cb1-158"><a href="#cb1-158" aria-hidden="true" tabindex="-1"></a>], columns <span class="op">=</span> [<span class="st">&quot;doi&quot;</span>, <span class="st">&quot;cited&quot;</span>, <span class="st">&quot;usage&quot;</span>, <span class="st">&quot;keywords&quot;</span>])</span>
<span id="cb1-159"><a href="#cb1-159" aria-hidden="true" tabindex="-1"></a> .drop_duplicates(<span class="st">&quot;doi&quot;</span>)</span>
<span id="cb1-160"><a href="#cb1-160" aria-hidden="true" tabindex="-1"></a> .assign(</span>
<span id="cb1-161"><a href="#cb1-161" aria-hidden="true" tabindex="-1"></a> inequality<span class="op">=</span><span class="kw">lambda</span> _df: _df[<span class="st">&quot;keywords&quot;</span>].<span class="bu">str</span>.replace(<span class="st">&quot;</span><span class="ch">\\</span><span class="st">&quot;</span>, <span class="st">&quot;&quot;</span>).<span class="bu">str</span>.extract(<span class="st">&#39;inequality::([\w\_]+),?&#39;</span>)</span>
<span id="cb1-162"><a href="#cb1-162" aria-hidden="true" tabindex="-1"></a> ).dropna(subset<span class="op">=</span><span class="st">&quot;inequality&quot;</span>)</span>
<span id="cb1-163"><a href="#cb1-163" aria-hidden="true" tabindex="-1"></a> .assign(</span>
<span id="cb1-164"><a href="#cb1-164" aria-hidden="true" tabindex="-1"></a> inequality<span class="op">=</span><span class="kw">lambda</span> _df: _df[<span class="st">&quot;inequality&quot;</span>].<span class="bu">str</span>.replace(<span class="st">&quot;_&quot;</span>, <span class="st">&quot; &quot;</span>),</span>
<span id="cb1-165"><a href="#cb1-165" aria-hidden="true" tabindex="-1"></a> projected <span class="op">=</span> <span class="dv">1</span></span>
<span id="cb1-166"><a href="#cb1-166" aria-hidden="true" tabindex="-1"></a> ).reset_index()</span>
<span id="cb1-167"><a href="#cb1-167" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb1-168"><a href="#cb1-168" aria-hidden="true" tabindex="-1"></a>pi</span>
<span id="cb1-169"><a href="#cb1-169" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-170"><a href="#cb1-170" aria-hidden="true" tabindex="-1"></a>inequality <span class="op">=</span> (pd.concat([</span>
<span id="cb1-171"><a href="#cb1-171" aria-hidden="true" tabindex="-1"></a> bib_df.groupby([<span class="st">&quot;author&quot;</span>, <span class="st">&quot;year&quot;</span>, <span class="st">&quot;title&quot;</span>])</span>
<span id="cb1-172"><a href="#cb1-172" aria-hidden="true" tabindex="-1"></a> .agg(</span>
<span id="cb1-173"><a href="#cb1-173" aria-hidden="true" tabindex="-1"></a> {</span>
<span id="cb1-174"><a href="#cb1-174" aria-hidden="true" tabindex="-1"></a> <span class="st">&quot;inequality&quot;</span>: <span class="kw">lambda</span> _col: <span class="st">&quot;; &quot;</span>.join(_col),</span>
<span id="cb1-175"><a href="#cb1-175" aria-hidden="true" tabindex="-1"></a> }</span>
<span id="cb1-176"><a href="#cb1-176" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb1-177"><a href="#cb1-177" aria-hidden="true" tabindex="-1"></a> .assign(</span>
<span id="cb1-178"><a href="#cb1-178" aria-hidden="true" tabindex="-1"></a> projected<span class="op">=</span><span class="dv">0</span></span>
<span id="cb1-179"><a href="#cb1-179" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-180"><a href="#cb1-180" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb1-181"><a href="#cb1-181" aria-hidden="true" tabindex="-1"></a> .reset_index()</span>
<span id="cb1-182"><a href="#cb1-182" aria-hidden="true" tabindex="-1"></a> .drop_duplicates() , pi])</span>
<span id="cb1-183"><a href="#cb1-183" aria-hidden="true" tabindex="-1"></a> .assign( inequality<span class="op">=</span><span class="kw">lambda</span> _df: _df[<span class="st">&quot;inequality&quot;</span>].<span class="bu">apply</span>(</span>
<span id="cb1-184"><a href="#cb1-184" aria-hidden="true" tabindex="-1"></a> <span class="kw">lambda</span> _cell: <span class="bu">set</span>([x.strip() <span class="cf">for</span> x <span class="kw">in</span> re.sub(<span class="vs">r&quot;\(.*\)&quot;</span>, <span class="st">&quot;&quot;</span>, _cell).split(<span class="st">&quot;;&quot;</span>)])</span>
<span id="cb1-185"><a href="#cb1-185" aria-hidden="true" tabindex="-1"></a> ),</span>
<span id="cb1-186"><a href="#cb1-186" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb1-187"><a href="#cb1-187" aria-hidden="true" tabindex="-1"></a> .explode(<span class="st">&quot;inequality&quot;</span>)</span>
<span id="cb1-188"><a href="#cb1-188" aria-hidden="true" tabindex="-1"></a>.drop_duplicates()</span>
<span id="cb1-189"><a href="#cb1-189" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb1-190"><a href="#cb1-190" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-191"><a href="#cb1-191" aria-hidden="true" tabindex="-1"></a>sort_order <span class="op">=</span> inequality[<span class="st">&quot;inequality&quot;</span>].value_counts().index</span>
<span id="cb1-192"><a href="#cb1-192" aria-hidden="true" tabindex="-1"></a>i <span class="op">=</span> inequality[inequality[<span class="st">&quot;inequality&quot;</span>].<span class="bu">str</span>.contains(<span class="vs">r&quot;(?:structural|institutional|agency)&quot;</span>) <span class="op">==</span> <span class="va">False</span>]</span>
<span id="cb1-193"><a href="#cb1-193" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> plt.figure()</span>
<span id="cb1-194"><a href="#cb1-194" aria-hidden="true" tabindex="-1"></a>fig.set_size_inches(<span class="dv">6</span>, <span class="dv">3</span>)</span>
<span id="cb1-195"><a href="#cb1-195" aria-hidden="true" tabindex="-1"></a>ax <span class="op">=</span> sns.countplot(i, x<span class="op">=</span><span class="st">&quot;inequality&quot;</span>, hue<span class="op">=</span><span class="st">&quot;projected&quot;</span> ,order<span class="op">=</span>i[<span class="st">&quot;inequality&quot;</span>].value_counts().index)</span>
<span id="cb1-196"><a href="#cb1-196" aria-hidden="true" tabindex="-1"></a>plt.setp(ax.get_xticklabels(), rotation<span class="op">=</span><span class="dv">45</span>, ha<span class="op">=</span><span class="st">&quot;right&quot;</span>,</span>
<span id="cb1-197"><a href="#cb1-197" aria-hidden="true" tabindex="-1"></a> rotation_mode<span class="op">=</span><span class="st">&quot;anchor&quot;</span>)</span>
<span id="cb1-198"><a href="#cb1-198" aria-hidden="true" tabindex="-1"></a>plt.show()</span>
<span id="cb1-199"><a href="#cb1-199" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb1-200"><a href="#cb1-200" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-201"><a href="#cb1-201" aria-hidden="true" tabindex="-1"></a><span class="fu"># Preliminary findings</span></span>
<span id="cb1-202"><a href="#cb1-202" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-205"><a href="#cb1-205" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb1-206"><a href="#cb1-206" aria-hidden="true" tabindex="-1"></a><span class="co">#| echo: false</span></span>
<span id="cb1-207"><a href="#cb1-207" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: fig-inequality-types</span></span>
<span id="cb1-208"><a href="#cb1-208" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-cap: Finished and projected inequality types</span></span>
<span id="cb1-209"><a href="#cb1-209" aria-hidden="true" tabindex="-1"></a>inequality <span class="op">=</span> (pd.concat([</span>
<span id="cb1-210"><a href="#cb1-210" aria-hidden="true" tabindex="-1"></a> bib_df.groupby([<span class="st">&quot;author&quot;</span>, <span class="st">&quot;year&quot;</span>, <span class="st">&quot;title&quot;</span>])</span>
<span id="cb1-211"><a href="#cb1-211" aria-hidden="true" tabindex="-1"></a> .agg(</span>
<span id="cb1-212"><a href="#cb1-212" aria-hidden="true" tabindex="-1"></a> {</span>
<span id="cb1-213"><a href="#cb1-213" aria-hidden="true" tabindex="-1"></a> <span class="st">&quot;inequality&quot;</span>: <span class="kw">lambda</span> _col: <span class="st">&quot;; &quot;</span>.join(_col),</span>
<span id="cb1-214"><a href="#cb1-214" aria-hidden="true" tabindex="-1"></a> }</span>
<span id="cb1-215"><a href="#cb1-215" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb1-216"><a href="#cb1-216" aria-hidden="true" tabindex="-1"></a> .assign(</span>
<span id="cb1-217"><a href="#cb1-217" aria-hidden="true" tabindex="-1"></a> projected<span class="op">=</span><span class="dv">0</span></span>
<span id="cb1-218"><a href="#cb1-218" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-219"><a href="#cb1-219" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb1-220"><a href="#cb1-220" aria-hidden="true" tabindex="-1"></a> .reset_index()</span>
<span id="cb1-221"><a href="#cb1-221" aria-hidden="true" tabindex="-1"></a> .drop_duplicates() , pi[pi[<span class="st">&quot;keywords&quot;</span>].<span class="bu">str</span>.contains(<span class="st">&quot;relevant&quot;</span>) <span class="op">==</span> <span class="va">True</span>]])</span>
<span id="cb1-222"><a href="#cb1-222" aria-hidden="true" tabindex="-1"></a> .assign( inequality<span class="op">=</span><span class="kw">lambda</span> _df: _df[<span class="st">&quot;inequality&quot;</span>].<span class="bu">apply</span>(</span>
<span id="cb1-223"><a href="#cb1-223" aria-hidden="true" tabindex="-1"></a> <span class="kw">lambda</span> _cell: <span class="bu">set</span>([x.strip() <span class="cf">for</span> x <span class="kw">in</span> re.sub(<span class="vs">r&quot;\(.*\)&quot;</span>, <span class="st">&quot;&quot;</span>, _cell).split(<span class="st">&quot;;&quot;</span>)])</span>
<span id="cb1-224"><a href="#cb1-224" aria-hidden="true" tabindex="-1"></a> ),</span>
<span id="cb1-225"><a href="#cb1-225" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb1-226"><a href="#cb1-226" aria-hidden="true" tabindex="-1"></a> .explode(<span class="st">&quot;inequality&quot;</span>)</span>
<span id="cb1-227"><a href="#cb1-227" aria-hidden="true" tabindex="-1"></a>.drop_duplicates()</span>
<span id="cb1-228"><a href="#cb1-228" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb1-229"><a href="#cb1-229" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-230"><a href="#cb1-230" aria-hidden="true" tabindex="-1"></a>sort_order <span class="op">=</span> inequality[<span class="st">&quot;inequality&quot;</span>].value_counts().index</span>
<span id="cb1-231"><a href="#cb1-231" aria-hidden="true" tabindex="-1"></a>i <span class="op">=</span> inequality[inequality[<span class="st">&quot;inequality&quot;</span>].<span class="bu">str</span>.contains(<span class="vs">r&quot;(?:structural|institutional|agency)&quot;</span>) <span class="op">==</span> <span class="va">False</span>]</span>
<span id="cb1-232"><a href="#cb1-232" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> plt.figure()</span>
<span id="cb1-233"><a href="#cb1-233" aria-hidden="true" tabindex="-1"></a>fig.set_size_inches(<span class="dv">6</span>, <span class="dv">3</span>)</span>
<span id="cb1-234"><a href="#cb1-234" aria-hidden="true" tabindex="-1"></a>ax <span class="op">=</span> sns.countplot(i, x<span class="op">=</span><span class="st">&quot;inequality&quot;</span>, hue<span class="op">=</span><span class="st">&quot;projected&quot;</span> ,order<span class="op">=</span>i[<span class="st">&quot;inequality&quot;</span>].value_counts().index)</span>
<span id="cb1-235"><a href="#cb1-235" aria-hidden="true" tabindex="-1"></a>plt.setp(ax.get_xticklabels(), rotation<span class="op">=</span><span class="dv">45</span>, ha<span class="op">=</span><span class="st">&quot;right&quot;</span>,</span>
<span id="cb1-236"><a href="#cb1-236" aria-hidden="true" tabindex="-1"></a> rotation_mode<span class="op">=</span><span class="st">&quot;anchor&quot;</span>)</span>
<span id="cb1-237"><a href="#cb1-237" aria-hidden="true" tabindex="-1"></a>plt.show()</span>
<span id="cb1-238"><a href="#cb1-238" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb1-239"><a href="#cb1-239" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-240"><a href="#cb1-240" aria-hidden="true" tabindex="-1"></a><span class="ss">- </span>interventions most strongly target gender-income divide</span>
<span id="cb1-241"><a href="#cb1-241" aria-hidden="true" tabindex="-1"></a><span class="ss"> - </span>most studies here recommend further scale-integration between agency/structural approaches</span>
<span id="cb1-242"><a href="#cb1-242" aria-hidden="true" tabindex="-1"></a><span class="ss"> - </span>most studies also only focus on analysing a single scale however</span>
<span id="cb1-243"><a href="#cb1-243" aria-hidden="true" tabindex="-1"></a><span class="ss">- </span>interventions often have intersectional impacts even if not targeted at them</span>
<span id="cb1-244"><a href="#cb1-244" aria-hidden="true" tabindex="-1"></a><span class="ss"> - </span>most visible for institutional/structural interventions and spatial inequalities</span>
<span id="cb1-245"><a href="#cb1-245" aria-hidden="true" tabindex="-1"></a><span class="ss"> - </span>studies analysing intersectional inequalities near unanimously recommend intersectional targeting</span>
<span id="cb1-246"><a href="#cb1-246" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-247"><a href="#cb1-247" aria-hidden="true" tabindex="-1"></a><span class="ss">- </span>individual agency-based interventions (training, subsidies, maternity benefits, transfers, microcredit, etc):</span>
<span id="cb1-248"><a href="#cb1-248" aria-hidden="true" tabindex="-1"></a><span class="ss"> - </span>seem most effective for targeting WoW outcomes of disability inequalities</span>
<span id="cb1-249"><a href="#cb1-249" aria-hidden="true" tabindex="-1"></a><span class="ss"> - </span>seem marginally effective for targeting WoW outcomes of gender inequalities</span>
<span id="cb1-250"><a href="#cb1-250" aria-hidden="true" tabindex="-1"></a><span class="ss"> - </span>require additional mediating scales for other inequalities</span>
<span id="cb1-251"><a href="#cb1-251" aria-hidden="true" tabindex="-1"></a><span class="ss">- </span>more structural interventions (education, infrastructural, ubi, trade liberalization, collective action):</span>
<span id="cb1-252"><a href="#cb1-252" aria-hidden="true" tabindex="-1"></a><span class="ss"> - </span>seem most effective for spatial, income, education-generational inequalities</span>
<span id="cb1-253"><a href="#cb1-253" aria-hidden="true" tabindex="-1"></a><span class="ss"> - </span>often show longer-term impacts, requiring longer periods of analyses</span>
<span id="cb1-254"><a href="#cb1-254" aria-hidden="true" tabindex="-1"></a><span class="ss"> - </span>can work without additional agency-based interventions, few studies analyse both at same time</span>
<span id="cb1-255"><a href="#cb1-255" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-256"><a href="#cb1-256" aria-hidden="true" tabindex="-1"></a><span class="fu"># Preliminary limitations</span></span>
<span id="cb1-257"><a href="#cb1-257" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-260"><a href="#cb1-260" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb1-261"><a href="#cb1-261" aria-hidden="true" tabindex="-1"></a><span class="co">#| echo: false</span></span>
<span id="cb1-262"><a href="#cb1-262" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: fig-intervention-types</span></span>
<span id="cb1-263"><a href="#cb1-263" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-cap: Finished and projected intervention types</span></span>
<span id="cb1-264"><a href="#cb1-264" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-265"><a href="#cb1-265" aria-hidden="true" tabindex="-1"></a><span class="co"># load zotero-based metadata: citations and uses</span></span>
<span id="cb1-266"><a href="#cb1-266" aria-hidden="true" tabindex="-1"></a>pi <span class="op">=</span> (pd.DataFrame([</span>
<span id="cb1-267"><a href="#cb1-267" aria-hidden="true" tabindex="-1"></a> [</span>
<span id="cb1-268"><a href="#cb1-268" aria-hidden="true" tabindex="-1"></a> entry[<span class="st">&quot;doi&quot;</span>] <span class="cf">if</span> <span class="st">&quot;doi&quot;</span> <span class="kw">in</span> entry.fields_dict <span class="cf">else</span> <span class="va">None</span>,</span>
<span id="cb1-269"><a href="#cb1-269" aria-hidden="true" tabindex="-1"></a> entry[<span class="st">&quot;times-cited&quot;</span>] <span class="cf">if</span> <span class="st">&quot;times-cited&quot;</span> <span class="kw">in</span> entry.fields_dict <span class="cf">else</span> <span class="va">None</span>,</span>
<span id="cb1-270"><a href="#cb1-270" aria-hidden="true" tabindex="-1"></a> entry[<span class="st">&quot;usage&quot;</span>] <span class="cf">if</span> <span class="st">&quot;usage&quot;</span> <span class="kw">in</span> entry.fields_dict <span class="cf">else</span> <span class="va">None</span>,</span>
<span id="cb1-271"><a href="#cb1-271" aria-hidden="true" tabindex="-1"></a> entry[<span class="st">&quot;keywords&quot;</span>] <span class="cf">if</span> <span class="st">&quot;keywords&quot;</span> <span class="kw">in</span> entry.fields_dict <span class="cf">else</span> <span class="va">None</span>,</span>
<span id="cb1-272"><a href="#cb1-272" aria-hidden="true" tabindex="-1"></a> ]</span>
<span id="cb1-273"><a href="#cb1-273" aria-hidden="true" tabindex="-1"></a> <span class="cf">for</span> entry <span class="kw">in</span> bib_sample.entries</span>
<span id="cb1-274"><a href="#cb1-274" aria-hidden="true" tabindex="-1"></a>], columns <span class="op">=</span> [<span class="st">&quot;doi&quot;</span>, <span class="st">&quot;cited&quot;</span>, <span class="st">&quot;usage&quot;</span>, <span class="st">&quot;keywords&quot;</span>])</span>
<span id="cb1-275"><a href="#cb1-275" aria-hidden="true" tabindex="-1"></a> .drop_duplicates(<span class="st">&quot;doi&quot;</span>)</span>
<span id="cb1-276"><a href="#cb1-276" aria-hidden="true" tabindex="-1"></a> .assign(</span>
<span id="cb1-277"><a href="#cb1-277" aria-hidden="true" tabindex="-1"></a> intervention<span class="op">=</span><span class="kw">lambda</span> _df: _df[<span class="st">&quot;keywords&quot;</span>].<span class="bu">str</span>.replace(<span class="st">&quot;</span><span class="ch">\\</span><span class="st">&quot;</span>, <span class="st">&quot;&quot;</span>).<span class="bu">str</span>.extract(<span class="st">&#39;type::([\w\_]+),?&#39;</span>)</span>
<span id="cb1-278"><a href="#cb1-278" aria-hidden="true" tabindex="-1"></a> ).dropna(subset<span class="op">=</span><span class="st">&quot;intervention&quot;</span>)</span>
<span id="cb1-279"><a href="#cb1-279" aria-hidden="true" tabindex="-1"></a> .assign(</span>
<span id="cb1-280"><a href="#cb1-280" aria-hidden="true" tabindex="-1"></a> intervention<span class="op">=</span><span class="kw">lambda</span> _df: _df[<span class="st">&quot;intervention&quot;</span>].<span class="bu">str</span>.replace(<span class="st">&quot;_&quot;</span>, <span class="st">&quot; &quot;</span>),</span>
<span id="cb1-281"><a href="#cb1-281" aria-hidden="true" tabindex="-1"></a> projected <span class="op">=</span> <span class="dv">1</span></span>
<span id="cb1-282"><a href="#cb1-282" aria-hidden="true" tabindex="-1"></a> ).reset_index()</span>
<span id="cb1-283"><a href="#cb1-283" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb1-284"><a href="#cb1-284" aria-hidden="true" tabindex="-1"></a>pi</span>
<span id="cb1-285"><a href="#cb1-285" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-286"><a href="#cb1-286" aria-hidden="true" tabindex="-1"></a>by_intervention <span class="op">=</span> (pd.concat([</span>
<span id="cb1-287"><a href="#cb1-287" aria-hidden="true" tabindex="-1"></a> bib_df.groupby([<span class="st">&quot;author&quot;</span>, <span class="st">&quot;year&quot;</span>, <span class="st">&quot;title&quot;</span>])</span>
<span id="cb1-288"><a href="#cb1-288" aria-hidden="true" tabindex="-1"></a> .agg(</span>
<span id="cb1-289"><a href="#cb1-289" aria-hidden="true" tabindex="-1"></a> {</span>
<span id="cb1-290"><a href="#cb1-290" aria-hidden="true" tabindex="-1"></a> <span class="st">&quot;intervention&quot;</span>: <span class="kw">lambda</span> _col: <span class="st">&quot;; &quot;</span>.join(_col),</span>
<span id="cb1-291"><a href="#cb1-291" aria-hidden="true" tabindex="-1"></a> }</span>
<span id="cb1-292"><a href="#cb1-292" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb1-293"><a href="#cb1-293" aria-hidden="true" tabindex="-1"></a> .assign(</span>
<span id="cb1-294"><a href="#cb1-294" aria-hidden="true" tabindex="-1"></a> projected<span class="op">=</span><span class="dv">0</span></span>
<span id="cb1-295"><a href="#cb1-295" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-296"><a href="#cb1-296" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb1-297"><a href="#cb1-297" aria-hidden="true" tabindex="-1"></a> .reset_index()</span>
<span id="cb1-298"><a href="#cb1-298" aria-hidden="true" tabindex="-1"></a> .drop_duplicates() , pi[pi[<span class="st">&quot;keywords&quot;</span>].<span class="bu">str</span>.contains(<span class="st">&quot;relevant&quot;</span>) <span class="op">==</span> <span class="va">True</span>]])</span>
<span id="cb1-299"><a href="#cb1-299" aria-hidden="true" tabindex="-1"></a> .assign( intervention<span class="op">=</span><span class="kw">lambda</span> _df: _df[<span class="st">&quot;intervention&quot;</span>].<span class="bu">apply</span>(</span>
<span id="cb1-300"><a href="#cb1-300" aria-hidden="true" tabindex="-1"></a> <span class="kw">lambda</span> _cell: <span class="bu">set</span>([x.strip() <span class="cf">for</span> x <span class="kw">in</span> re.sub(<span class="vs">r&quot;\(.*\)&quot;</span>, <span class="st">&quot;&quot;</span>, _cell).split(<span class="st">&quot;;&quot;</span>)])</span>
<span id="cb1-301"><a href="#cb1-301" aria-hidden="true" tabindex="-1"></a> ),</span>
<span id="cb1-302"><a href="#cb1-302" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb1-303"><a href="#cb1-303" aria-hidden="true" tabindex="-1"></a> .explode(<span class="st">&quot;intervention&quot;</span>)</span>
<span id="cb1-304"><a href="#cb1-304" aria-hidden="true" tabindex="-1"></a>.drop_duplicates()</span>
<span id="cb1-305"><a href="#cb1-305" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb1-306"><a href="#cb1-306" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-307"><a href="#cb1-307" aria-hidden="true" tabindex="-1"></a>sort_order <span class="op">=</span> by_intervention[<span class="st">&quot;intervention&quot;</span>].value_counts().index</span>
<span id="cb1-308"><a href="#cb1-308" aria-hidden="true" tabindex="-1"></a>i <span class="op">=</span> by_intervention[by_intervention[<span class="st">&quot;intervention&quot;</span>].<span class="bu">str</span>.contains(<span class="vs">r&quot;(?:structural|institutional|agency)&quot;</span>) <span class="op">==</span> <span class="va">False</span>]</span>
<span id="cb1-309"><a href="#cb1-309" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> plt.figure()</span>
<span id="cb1-310"><a href="#cb1-310" aria-hidden="true" tabindex="-1"></a>fig.set_size_inches(<span class="dv">6</span>, <span class="dv">3</span>)</span>
<span id="cb1-311"><a href="#cb1-311" aria-hidden="true" tabindex="-1"></a>ax <span class="op">=</span> sns.countplot(i, x<span class="op">=</span><span class="st">&quot;intervention&quot;</span>, hue<span class="op">=</span><span class="st">&quot;projected&quot;</span> ,order<span class="op">=</span>i[<span class="st">&quot;intervention&quot;</span>].value_counts().index)</span>
<span id="cb1-312"><a href="#cb1-312" aria-hidden="true" tabindex="-1"></a>plt.setp(ax.get_xticklabels(), rotation<span class="op">=</span><span class="dv">45</span>, ha<span class="op">=</span><span class="st">&quot;right&quot;</span>,</span>
<span id="cb1-313"><a href="#cb1-313" aria-hidden="true" tabindex="-1"></a> rotation_mode<span class="op">=</span><span class="st">&quot;anchor&quot;</span>)</span>
<span id="cb1-314"><a href="#cb1-314" aria-hidden="true" tabindex="-1"></a>plt.show()</span>
<span id="cb1-315"><a href="#cb1-315" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb1-316"><a href="#cb1-316" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-317"><a href="#cb1-317" aria-hidden="true" tabindex="-1"></a><span class="ss">- </span>stronger institutional-structural research focus in developed countries, with more structural-agency based in developing countries</span>
<span id="cb1-318"><a href="#cb1-318" aria-hidden="true" tabindex="-1"></a><span class="ss">- </span>employment creation as a category is often subsumed in other structural/institutional analyses</span>
<span id="cb1-319"><a href="#cb1-319" aria-hidden="true" tabindex="-1"></a><span class="ss">- </span>little evidence-based research on effect of interventions targeting education on world of work outcomes</span>
<span id="cb1-320"><a href="#cb1-320" aria-hidden="true" tabindex="-1"></a><span class="ss">- </span>spatial inequality most evenly geographically spread evidence base</span>
<span id="cb1-321"><a href="#cb1-321" aria-hidden="true" tabindex="-1"></a><span class="ss">- </span>empirical base on interventions targeting disability inequalities strongly restricted on developed countries, especially United States</span>
<span id="cb1-322"><a href="#cb1-322" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-325"><a href="#cb1-325" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb1-326"><a href="#cb1-326" aria-hidden="true" tabindex="-1"></a><span class="co">#| echo: false</span></span>
<span id="cb1-327"><a href="#cb1-327" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: fig-countries</span></span>
<span id="cb1-328"><a href="#cb1-328" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-cap: Country spread</span></span>
<span id="cb1-329"><a href="#cb1-329" aria-hidden="true" tabindex="-1"></a><span class="co">#| column: screen</span></span>
<span id="cb1-330"><a href="#cb1-330" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-331"><a href="#cb1-331" aria-hidden="true" tabindex="-1"></a><span class="co"># load zotero-based metadata: citations and uses</span></span>
<span id="cb1-332"><a href="#cb1-332" aria-hidden="true" tabindex="-1"></a>pi <span class="op">=</span> (pd.DataFrame([</span>
<span id="cb1-333"><a href="#cb1-333" aria-hidden="true" tabindex="-1"></a> [</span>
<span id="cb1-334"><a href="#cb1-334" aria-hidden="true" tabindex="-1"></a> entry[<span class="st">&quot;doi&quot;</span>] <span class="cf">if</span> <span class="st">&quot;doi&quot;</span> <span class="kw">in</span> entry.fields_dict <span class="cf">else</span> <span class="va">None</span>,</span>
<span id="cb1-335"><a href="#cb1-335" aria-hidden="true" tabindex="-1"></a> entry[<span class="st">&quot;times-cited&quot;</span>] <span class="cf">if</span> <span class="st">&quot;times-cited&quot;</span> <span class="kw">in</span> entry.fields_dict <span class="cf">else</span> <span class="va">None</span>,</span>
<span id="cb1-336"><a href="#cb1-336" aria-hidden="true" tabindex="-1"></a> entry[<span class="st">&quot;usage&quot;</span>] <span class="cf">if</span> <span class="st">&quot;usage&quot;</span> <span class="kw">in</span> entry.fields_dict <span class="cf">else</span> <span class="va">None</span>,</span>
<span id="cb1-337"><a href="#cb1-337" aria-hidden="true" tabindex="-1"></a> entry[<span class="st">&quot;keywords&quot;</span>] <span class="cf">if</span> <span class="st">&quot;keywords&quot;</span> <span class="kw">in</span> entry.fields_dict <span class="cf">else</span> <span class="va">None</span>,</span>
<span id="cb1-338"><a href="#cb1-338" aria-hidden="true" tabindex="-1"></a> ]</span>
<span id="cb1-339"><a href="#cb1-339" aria-hidden="true" tabindex="-1"></a> <span class="cf">for</span> entry <span class="kw">in</span> bib_sample.entries</span>
<span id="cb1-340"><a href="#cb1-340" aria-hidden="true" tabindex="-1"></a>], columns <span class="op">=</span> [<span class="st">&quot;doi&quot;</span>, <span class="st">&quot;cited&quot;</span>, <span class="st">&quot;usage&quot;</span>, <span class="st">&quot;keywords&quot;</span>])</span>
<span id="cb1-341"><a href="#cb1-341" aria-hidden="true" tabindex="-1"></a> .drop_duplicates(<span class="st">&quot;doi&quot;</span>)</span>
<span id="cb1-342"><a href="#cb1-342" aria-hidden="true" tabindex="-1"></a> .assign(</span>
<span id="cb1-343"><a href="#cb1-343" aria-hidden="true" tabindex="-1"></a> country<span class="op">=</span><span class="kw">lambda</span> _df: _df[<span class="st">&quot;keywords&quot;</span>].<span class="bu">str</span>.replace(<span class="st">&quot;</span><span class="ch">\\</span><span class="st">&quot;</span>, <span class="st">&quot;&quot;</span>).<span class="bu">str</span>.extract(<span class="st">&#39;country::([\w\_]+),?&#39;</span>)</span>
<span id="cb1-344"><a href="#cb1-344" aria-hidden="true" tabindex="-1"></a> ).dropna(subset<span class="op">=</span><span class="st">&quot;country&quot;</span>)</span>
<span id="cb1-345"><a href="#cb1-345" aria-hidden="true" tabindex="-1"></a> .assign(</span>
<span id="cb1-346"><a href="#cb1-346" aria-hidden="true" tabindex="-1"></a> country<span class="op">=</span><span class="kw">lambda</span> _df: _df[<span class="st">&quot;country&quot;</span>].<span class="bu">str</span>.replace(<span class="st">&quot;_&quot;</span>, <span class="st">&quot; &quot;</span>).<span class="bu">str</span>.replace(<span class="st">&quot;US&quot;</span>, <span class="st">&quot;United States&quot;</span>).<span class="bu">str</span>.replace(<span class="st">&quot;Britain&quot;</span>, <span class="st">&quot;United Kingdom&quot;</span>),</span>
<span id="cb1-347"><a href="#cb1-347" aria-hidden="true" tabindex="-1"></a> projected <span class="op">=</span> <span class="dv">1</span></span>
<span id="cb1-348"><a href="#cb1-348" aria-hidden="true" tabindex="-1"></a> ).reset_index()</span>
<span id="cb1-349"><a href="#cb1-349" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb1-350"><a href="#cb1-350" aria-hidden="true" tabindex="-1"></a>pi</span>
<span id="cb1-351"><a href="#cb1-351" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-352"><a href="#cb1-352" aria-hidden="true" tabindex="-1"></a>by_country <span class="op">=</span> (pd.concat([</span>
<span id="cb1-353"><a href="#cb1-353" aria-hidden="true" tabindex="-1"></a> bib_df.groupby([<span class="st">&quot;author&quot;</span>, <span class="st">&quot;year&quot;</span>, <span class="st">&quot;title&quot;</span>])</span>
<span id="cb1-354"><a href="#cb1-354" aria-hidden="true" tabindex="-1"></a> .agg(</span>
<span id="cb1-355"><a href="#cb1-355" aria-hidden="true" tabindex="-1"></a> {</span>
<span id="cb1-356"><a href="#cb1-356" aria-hidden="true" tabindex="-1"></a> <span class="st">&quot;country&quot;</span>: <span class="kw">lambda</span> _col: <span class="st">&quot;; &quot;</span>.join(_col),</span>
<span id="cb1-357"><a href="#cb1-357" aria-hidden="true" tabindex="-1"></a> }</span>
<span id="cb1-358"><a href="#cb1-358" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb1-359"><a href="#cb1-359" aria-hidden="true" tabindex="-1"></a> .assign(</span>
<span id="cb1-360"><a href="#cb1-360" aria-hidden="true" tabindex="-1"></a> projected<span class="op">=</span><span class="dv">0</span></span>
<span id="cb1-361"><a href="#cb1-361" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-362"><a href="#cb1-362" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb1-363"><a href="#cb1-363" aria-hidden="true" tabindex="-1"></a> .reset_index()</span>
<span id="cb1-364"><a href="#cb1-364" aria-hidden="true" tabindex="-1"></a> .drop_duplicates() , pi[pi[<span class="st">&quot;keywords&quot;</span>].<span class="bu">str</span>.contains(<span class="st">&quot;relevant&quot;</span>) <span class="op">==</span> <span class="va">True</span>]])</span>
<span id="cb1-365"><a href="#cb1-365" aria-hidden="true" tabindex="-1"></a> .assign( country<span class="op">=</span><span class="kw">lambda</span> _df: _df[<span class="st">&quot;country&quot;</span>].<span class="bu">apply</span>(</span>
<span id="cb1-366"><a href="#cb1-366" aria-hidden="true" tabindex="-1"></a> <span class="kw">lambda</span> _cell: <span class="bu">set</span>([x.strip() <span class="cf">for</span> x <span class="kw">in</span> re.sub(<span class="vs">r&quot;\(.*\)&quot;</span>, <span class="st">&quot;&quot;</span>, _cell).split(<span class="st">&quot;;&quot;</span>)])</span>
<span id="cb1-367"><a href="#cb1-367" aria-hidden="true" tabindex="-1"></a> ),</span>
<span id="cb1-368"><a href="#cb1-368" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb1-369"><a href="#cb1-369" aria-hidden="true" tabindex="-1"></a> .explode(<span class="st">&quot;country&quot;</span>)</span>
<span id="cb1-370"><a href="#cb1-370" aria-hidden="true" tabindex="-1"></a>.drop_duplicates()</span>
<span id="cb1-371"><a href="#cb1-371" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb1-372"><a href="#cb1-372" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-373"><a href="#cb1-373" aria-hidden="true" tabindex="-1"></a>sort_order <span class="op">=</span> by_country[<span class="st">&quot;country&quot;</span>].value_counts().index</span>
<span id="cb1-374"><a href="#cb1-374" aria-hidden="true" tabindex="-1"></a>i <span class="op">=</span> by_country[by_country[<span class="st">&quot;country&quot;</span>].<span class="bu">str</span>.contains(<span class="vs">r&quot;(?:structural|institutional|agency)&quot;</span>) <span class="op">==</span> <span class="va">False</span>]</span>
<span id="cb1-375"><a href="#cb1-375" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> plt.figure()</span>
<span id="cb1-376"><a href="#cb1-376" aria-hidden="true" tabindex="-1"></a>fig.set_size_inches(<span class="dv">12</span>, <span class="dv">5</span>)</span>
<span id="cb1-377"><a href="#cb1-377" aria-hidden="true" tabindex="-1"></a>ax <span class="op">=</span> sns.countplot(i, x<span class="op">=</span><span class="st">&quot;country&quot;</span>, hue<span class="op">=</span><span class="st">&quot;projected&quot;</span> ,order<span class="op">=</span>i[<span class="st">&quot;country&quot;</span>].value_counts().index)</span>
<span id="cb1-378"><a href="#cb1-378" aria-hidden="true" tabindex="-1"></a>plt.setp(ax.get_xticklabels(), rotation<span class="op">=</span><span class="dv">45</span>, ha<span class="op">=</span><span class="st">&quot;right&quot;</span>,</span>
<span id="cb1-379"><a href="#cb1-379" aria-hidden="true" tabindex="-1"></a> rotation_mode<span class="op">=</span><span class="st">&quot;anchor&quot;</span>)</span>
<span id="cb1-380"><a href="#cb1-380" aria-hidden="true" tabindex="-1"></a>plt.show()</span>
<span id="cb1-381"><a href="#cb1-381" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
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