fix(repo): Rename all references to data and output dir

This commit is contained in:
Marty Oehme 2024-07-15 21:38:05 +02:00
parent f384515737
commit b4730f6ea8
Signed by: Marty
GPG key ID: EDBF2ED917B2EF6A
22 changed files with 67 additions and 69 deletions

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@ -4,14 +4,14 @@
This repository contains all data, modelling and processing source code and the complete textual content to reproduce the scoping review study.
The most up-to-date version of this repository can always be found [here](https://git.martyoeh.me/professional/wow-inequalities).
Raw, intermediate and processed data can all be found in the `02-data/` directory:
Raw, intermediate and processed data can all be found in the `data/` directory:
Raw data include the unmodified database queries using the scoping review search terms.
Intermediate data are made up of the bibtex file produced by Zotero, after tagging and sorting in a Zotero library, ready to be re-imported into the application.
Processed data include the fully extracted studies which make up the main sample for the review.
The full article text and code can be found in the `scoping_review.qmd` file.
It makes use of supplementary processing code which resides in the `src/` directory,
mainly to load processed data from the `02-data/` directory and turn it into `.csv` data,
mainly to load processed data from the `data/` directory and turn it into `.csv` data,
as well as pre-processing those for visualization and validity ranking within the study.
## Execution and Reproduction
@ -35,6 +35,6 @@ Now, by invoking `make` the project can be rendered:
make
```
Make will by default extract the processed data and use it to render the full project into a pdf, an html and a docx version of the review, which are deposited in the `04-outputs/` directory.
Make will by default extract the processed data and use it to render the full project into a pdf, an html and a docx version of the review, which are deposited in the `outputs/` directory.
You can invoke any of the `extract`, `render`, `release` steps manually instead by executing e.g. `make extract`.

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@ -16,11 +16,11 @@ format:
theme: darkly
docx:
filters:
# - pandoc-to-zotero-live
- pandoc-to-zotero-live
- docx-landscape
echo: false
number-sections: true
reference-doc: 02-data/supplementary/justified.docx
reference-doc: data/supplementary/justified.docx
elsevier-pdf:
echo: false
number-sections: true

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@ -1,6 +1,4 @@
project:
title: "Key terms and definitions"
output-dir: 04-outputs
render:
- presentation_summary.md
- notes.qmd
@ -21,7 +19,7 @@ format:
docx:
echo: false
number-sections: true
reference-doc: 02-data/supplementary/justified.docx
reference-doc: data/supplementary/justified.docx
filters:
- pandoc-to-zotero-live
- docx-landscape

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@ -1,8 +1,8 @@
project:
output-dir: 04-outputs
output-dir: output
execute-dir: project
bibliography: 02-data/intermediate/zotero-library.bib
bibliography: data/intermediate/zotero-library.bib
csl: /home/marty/documents/library/utilities/styles/APA-7.csl
zoterolive:
library: wow-inequalities

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@ -102,7 +102,7 @@ with a focus on the narrowing criteria specified in @tbl-inclusion-criteria.
::: {#tbl-inclusion-criteria}
```{python}
inclusion_criteria = pd.read_csv("02-data/supplementary/inclusion-criteria.tsv", sep="\t")
inclusion_criteria = pd.read_csv("data/supplementary/inclusion-criteria.tsv", sep="\t")
Markdown(tabulate(inclusion_criteria, showindex=False, headers="keys", tablefmt="grid"))
```
@ -152,7 +152,7 @@ ultimately resulting in the process represented in the PRISMA chart in @fig-pris
```{mermaid}
%%| label: fig-prisma
%%| fig-cap: PRISMA flowchart for scoping process
%%| file: 02-data/processed/prisma.mmd
%%| file: data/processed/prisma.mmd
```
All relevant data concerning both their major findings and statistical significance are then extracted from the individual studies into a collective results matrix.
@ -225,7 +225,7 @@ def strength_for(val):
]
findings_institutional = pd.read_csv("02-data/supplementary/findings-institutional.csv")
findings_institutional = pd.read_csv("data/supplementary/findings-institutional.csv")
outp = Markdown(
tabulate(
@ -695,7 +695,7 @@ Another reason could be the actual implementation of different policy programmes
::: {#appatbl-wow-terms}
```{python}
terms_wow = pd.read_csv("02-data/supplementary/terms_wow.csv")
terms_wow = pd.read_csv("data/supplementary/terms_wow.csv")
Markdown(tabulate(terms_wow.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
```
@ -706,7 +706,7 @@ World of work term cluster
::: {#appatbl-intervention-terms}
```{python}
terms_policy = pd.read_csv("02-data/supplementary/terms_policy.csv")
terms_policy = pd.read_csv("data/supplementary/terms_policy.csv")
# different headers to include 'social norms'
headers = ["General", "Institutional", "Structural", "Agency & social norms"]
Markdown(tabulate(terms_policy.fillna(""), showindex=False, headers=headers, tablefmt="grid"))
@ -719,7 +719,7 @@ Policy intervention term cluster
::: {#appatbl-inequality-terms}
```{python}
terms_inequality = pd.read_csv("02-data/supplementary/terms_inequality.csv")
terms_inequality = pd.read_csv("data/supplementary/terms_inequality.csv")
Markdown(tabulate(terms_inequality.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
```

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@ -1,6 +1,6 @@
# Summary of study findings
written into 02-data/supplementary/findings-*.csv tables
written into data/supplementary/findings-*.csv tables
## Institutional

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@ -1,6 +1,6 @@
# Validity estimators
For a general concept description see ../03-documentation/terms_of_reference-key_terms.md#validity
For a general concept description see ../documentation/terms_of_reference-key_terms.md#validity
From Maitrot2017 -> Section 4, Figure 3 and Appendix table notes
They rank *only* quasi-experimental/experimental

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@ -3757,7 +3757,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
});
</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"> 02-data/intermediate/zotero-library.bib</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>
<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>
@ -3788,7 +3788,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
<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;./02-data&quot;</span>)</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>
<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>
@ -3860,7 +3860,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
<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 02-data/supplementary undeduplciated counts to get database starting and snowballing counts</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>
<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>

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@ -5125,7 +5125,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
});
</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="cb2" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="an">bibliography:</span><span class="co"> 02-data/intermediate/zotero-library.bib</span></span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="an">bibliography:</span><span class="co"> data/intermediate/zotero-library.bib</span></span>
<span id="cb2-3"><a href="#cb2-3" 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="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a><span class="an">papersize:</span><span class="co"> A4</span></span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a><span class="an">linestretch:</span><span class="co"> 1.5</span></span>
@ -5148,7 +5148,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
<span id="cb2-24"><a href="#cb2-24" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb2-25"><a href="#cb2-25" aria-hidden="true" tabindex="-1"></a><span class="co">#| echo: false</span></span>
<span id="cb2-26"><a href="#cb2-26" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> pathlib <span class="im">import</span> Path</span>
<span id="cb2-27"><a href="#cb2-27" aria-hidden="true" tabindex="-1"></a>DATA_DIR<span class="op">=</span>Path(<span class="st">&quot;./02-data&quot;</span>)</span>
<span id="cb2-27"><a href="#cb2-27" aria-hidden="true" tabindex="-1"></a>DATA_DIR<span class="op">=</span>Path(<span class="st">&quot;./data&quot;</span>)</span>
<span id="cb2-28"><a href="#cb2-28" 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="cb2-29"><a href="#cb2-29" 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="cb2-30"><a href="#cb2-30" aria-hidden="true" tabindex="-1"></a>PROCESSED_DATA<span class="op">=</span>DATA_DIR.joinpath(<span class="st">&quot;processed&quot;</span>)</span>
@ -5522,7 +5522,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
<span id="cb2-400"><a href="#cb2-400" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: tbl-inclusion-criteria</span></span>
<span id="cb2-401"><a href="#cb2-401" aria-hidden="true" tabindex="-1"></a><span class="co">#| tbl-cap: Study inclusion and exclusion scoping criteria {#tbl-inclusion-criteria}</span></span>
<span id="cb2-402"><a href="#cb2-402" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-403"><a href="#cb2-403" aria-hidden="true" tabindex="-1"></a>inclusion_criteria <span class="op">=</span> pd.read_csv(<span class="st">&quot;02-data/supplementary/inclusion-criteria.tsv&quot;</span>, sep<span class="op">=</span><span class="st">&quot;</span><span class="ch">\t</span><span class="st">&quot;</span>)</span>
<span id="cb2-403"><a href="#cb2-403" aria-hidden="true" tabindex="-1"></a>inclusion_criteria <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/inclusion-criteria.tsv&quot;</span>, sep<span class="op">=</span><span class="st">&quot;</span><span class="ch">\t</span><span class="st">&quot;</span>)</span>
<span id="cb2-404"><a href="#cb2-404" aria-hidden="true" tabindex="-1"></a>md(tabulate(inclusion_criteria, showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span><span class="st">&quot;keys&quot;</span>, tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span>
<span id="cb2-405"><a href="#cb2-405" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb2-406"><a href="#cb2-406" aria-hidden="true" tabindex="-1"></a></span>

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@ -5378,7 +5378,7 @@ Table 1: ILO focus areas for inequality reduction
<p>To identify potential studies and create an initial sample, relevant terms for the clusters of world of work, inequality and policy interventions have been extracted from the existing reviews as well as the ILO definitions. Identified terms comprising the world of work can be found in <a href="#tbl-wow-terms" class="quarto-xref">Table 2</a>, with the search query requiring a term from the general column and one other column.</p>
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a>terms_wow <span class="op">=</span> pd.read_csv(<span class="st">&quot;02-data/supplementary/terms_wow.csv&quot;</span>)</span>
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a>terms_wow <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/terms_wow.csv&quot;</span>)</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>md(tabulate(terms_wow.fillna(<span class="st">&quot;&quot;</span>), showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span><span class="st">&quot;keys&quot;</span>, tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div id="tbl-wow-terms" class="quarto-float anchored">
@ -5483,7 +5483,7 @@ Table 2: World of work term cluster
<p>For the policy intervention cluster, a variety of terms have been identified both from the ILO policy areas and guidelines as well as existing reviews, as can be seen in <a href="#tbl-intervention-terms" class="quarto-xref">Table 3</a>. Where terms have been identified from previous reviews outside the introduced ILO policy guidelines, there source has been included in the table. For the database query, a single term from the general category is required to be included in addition to one term from <em>any</em> of the remaining categories.</p>
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>terms_policy <span class="op">=</span> pd.read_csv(<span class="st">&quot;02-data/supplementary/terms_policy.csv&quot;</span>)</span>
<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>terms_policy <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/terms_policy.csv&quot;</span>)</span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="co"># different headers to include &#39;social norms&#39;</span></span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>headers <span class="op">=</span> [<span class="st">&quot;General&quot;</span>, <span class="st">&quot;Institutional&quot;</span>, <span class="st">&quot;Structural&quot;</span>, <span class="st">&quot;Agency &amp; social norms&quot;</span>]</span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a>md(tabulate(terms_policy.fillna(<span class="st">&quot;&quot;</span>), showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span>headers, tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@ -5622,7 +5622,7 @@ Table 3: Policy intervention term cluster
<div class="cell" data-execution_count="4">
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>terms_inequality <span class="op">=</span> pd.read_csv(<span class="st">&quot;02-data/supplementary/terms_inequality.csv&quot;</span>)</span>
<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>terms_inequality <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/terms_inequality.csv&quot;</span>)</span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>md(tabulate(terms_inequality.fillna(<span class="st">&quot;&quot;</span>), showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span><span class="st">&quot;keys&quot;</span>, tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div id="tbl-inequality-terms" class="cell quarto-float anchored" data-execution_count="4">
@ -5759,7 +5759,7 @@ Table 4: Inequality term cluster
<p>An overview of the respective criteria used for inclusion or exclusion can be found in <a href="#tbl-inclusion-criteria" class="quarto-xref">Table 5</a>. It restricts studies to those that comprise primary research published after 2000, with a focus on the narrowing criteria specified in <a href="#tbl-inclusion-criteria" class="quarto-xref">Table 5</a>.</p>
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>inclusion_criteria <span class="op">=</span> pd.read_csv(<span class="st">&quot;02-data/supplementary/inclusion-criteria.tsv&quot;</span>, sep<span class="op">=</span><span class="st">&quot;</span><span class="ch">\t</span><span class="st">&quot;</span>)</span>
<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>inclusion_criteria <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/inclusion-criteria.tsv&quot;</span>, sep<span class="op">=</span><span class="st">&quot;</span><span class="ch">\t</span><span class="st">&quot;</span>)</span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>md(tabulate(inclusion_criteria, showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span><span class="st">&quot;keys&quot;</span>, tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div id="tbl-inclusion-criteria" class="quarto-float anchored">
@ -9294,7 +9294,7 @@ Figure 4: Available studies by primary type of intervention
<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> strength_for(val):</span>
<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> <span class="bu">list</span>(study_strength_bins.keys())[<span class="bu">list</span>(study_strength_bins.values()).index(val)]</span>
<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a>findings_institutional <span class="op">=</span> pd.read_csv(<span class="st">&quot;02-data/supplementary/findings-institutional.csv&quot;</span>)</span>
<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a>findings_institutional <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/findings-institutional.csv&quot;</span>)</span>
<span id="cb8-12"><a href="#cb8-12" aria-hidden="true" tabindex="-1"></a>fd_df <span class="op">=</span> validity.add_to_findings(findings_institutional, by_intervention, study_strength_bins)</span>
<span id="cb8-13"><a href="#cb8-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-14"><a href="#cb8-14" aria-hidden="true" tabindex="-1"></a>md(tabulate(fd_df[[<span class="st">&quot;area of policy&quot;</span>, <span class="st">&quot;internal_validity&quot;</span>, <span class="st">&quot;external_validity&quot;</span>, <span class="st">&quot;findings&quot;</span>, <span class="st">&quot;channels&quot;</span>]].fillna(<span class="st">&quot;&quot;</span>), showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span>[<span class="st">&quot;area of policy&quot;</span>, <span class="st">&quot;internal strength&quot;</span>, <span class="st">&quot;external strength&quot;</span>, <span class="st">&quot;main findings&quot;</span>, <span class="st">&quot;channels&quot;</span>], tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@ -9558,7 +9558,7 @@ Table 6: Summary of main findings for institutional policies
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> src.model <span class="im">import</span> validity</span>
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>findings_structural <span class="op">=</span> pd.read_csv(<span class="st">&quot;02-data/supplementary/findings-structural.csv&quot;</span>)</span>
<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>findings_structural <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/findings-structural.csv&quot;</span>)</span>
<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a>fd_df <span class="op">=</span> validity.add_to_findings(findings_structural, by_intervention, study_strength_bins)</span>
<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a>md(tabulate(fd_df[[<span class="st">&quot;area of policy&quot;</span>, <span class="st">&quot;internal_validity&quot;</span>, <span class="st">&quot;external_validity&quot;</span>, <span class="st">&quot;findings&quot;</span>, <span class="st">&quot;channels&quot;</span>]].fillna(<span class="st">&quot;&quot;</span>), showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span>[<span class="st">&quot;area of policy&quot;</span>, <span class="st">&quot;internal strength&quot;</span>, <span class="st">&quot;external strength&quot;</span>, <span class="st">&quot;main findings&quot;</span>, <span class="st">&quot;channels&quot;</span>], tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@ -9794,7 +9794,7 @@ Table 7: Summary of main findings for structural policies
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> src.model <span class="im">import</span> validity</span>
<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>findings_agency <span class="op">=</span> pd.read_csv(<span class="st">&quot;02-data/supplementary/findings-agency.csv&quot;</span>)</span>
<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>findings_agency <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/findings-agency.csv&quot;</span>)</span>
<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a>fd_df <span class="op">=</span> validity.add_to_findings(findings_agency, by_intervention, study_strength_bins)</span>
<span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb10-6"><a href="#cb10-6" aria-hidden="true" tabindex="-1"></a>md(tabulate(fd_df[[<span class="st">&quot;area of policy&quot;</span>, <span class="st">&quot;internal_validity&quot;</span>, <span class="st">&quot;external_validity&quot;</span>, <span class="st">&quot;findings&quot;</span>, <span class="st">&quot;channels&quot;</span>]].fillna(<span class="st">&quot;&quot;</span>), showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span>[<span class="st">&quot;area of policy&quot;</span>, <span class="st">&quot;internal strength&quot;</span>, <span class="st">&quot;external strength&quot;</span>, <span class="st">&quot;main findings&quot;</span>, <span class="st">&quot;channels&quot;</span>], tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@ -22545,7 +22545,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
<span id="cb26-39"><a href="#cb26-39" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-40"><a href="#cb26-40" aria-hidden="true" tabindex="-1"></a>sns.set_style(<span class="st">&quot;whitegrid&quot;</span>)</span>
<span id="cb26-41"><a href="#cb26-41" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-42"><a href="#cb26-42" aria-hidden="true" tabindex="-1"></a>DATA_DIR<span class="op">=</span>Path(<span class="st">&quot;./02-data&quot;</span>)</span>
<span id="cb26-42"><a href="#cb26-42" aria-hidden="true" tabindex="-1"></a>DATA_DIR<span class="op">=</span>Path(<span class="st">&quot;./data&quot;</span>)</span>
<span id="cb26-43"><a href="#cb26-43" 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="cb26-44"><a href="#cb26-44" 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="cb26-45"><a href="#cb26-45" aria-hidden="true" tabindex="-1"></a>PROCESSED_DATA<span class="op">=</span>DATA_DIR.joinpath(<span class="st">&quot;processed&quot;</span>)</span>
@ -22835,7 +22835,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
<span id="cb26-331"><a href="#cb26-331" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-332"><a href="#cb26-332" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: tbl-wow-terms</span></span>
<span id="cb26-333"><a href="#cb26-333" aria-hidden="true" tabindex="-1"></a><span class="co">#| tbl-cap: World of work term cluster</span></span>
<span id="cb26-334"><a href="#cb26-334" aria-hidden="true" tabindex="-1"></a>terms_wow <span class="op">=</span> pd.read_csv(<span class="st">&quot;02-data/supplementary/terms_wow.csv&quot;</span>)</span>
<span id="cb26-334"><a href="#cb26-334" aria-hidden="true" tabindex="-1"></a>terms_wow <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/terms_wow.csv&quot;</span>)</span>
<span id="cb26-335"><a href="#cb26-335" aria-hidden="true" tabindex="-1"></a>md(tabulate(terms_wow.fillna(<span class="st">&quot;&quot;</span>), showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span><span class="st">&quot;keys&quot;</span>, tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span>
<span id="cb26-336"><a href="#cb26-336" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-337"><a href="#cb26-337" aria-hidden="true" tabindex="-1"></a></span>
@ -22857,7 +22857,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
<span id="cb26-355"><a href="#cb26-355" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-356"><a href="#cb26-356" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: tbl-intervention-terms</span></span>
<span id="cb26-357"><a href="#cb26-357" aria-hidden="true" tabindex="-1"></a><span class="co">#| tbl-cap: Intervention term cluster</span></span>
<span id="cb26-358"><a href="#cb26-358" aria-hidden="true" tabindex="-1"></a>terms_policy <span class="op">=</span> pd.read_csv(<span class="st">&quot;02-data/supplementary/terms_policy.csv&quot;</span>)</span>
<span id="cb26-358"><a href="#cb26-358" aria-hidden="true" tabindex="-1"></a>terms_policy <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/terms_policy.csv&quot;</span>)</span>
<span id="cb26-359"><a href="#cb26-359" aria-hidden="true" tabindex="-1"></a><span class="co"># different headers to include &#39;social norms&#39;</span></span>
<span id="cb26-360"><a href="#cb26-360" aria-hidden="true" tabindex="-1"></a>headers <span class="op">=</span> [<span class="st">&quot;General&quot;</span>, <span class="st">&quot;Institutional&quot;</span>, <span class="st">&quot;Structural&quot;</span>, <span class="st">&quot;Agency &amp; social norms&quot;</span>]</span>
<span id="cb26-361"><a href="#cb26-361" aria-hidden="true" tabindex="-1"></a>md(tabulate(terms_policy.fillna(<span class="st">&quot;&quot;</span>), showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span>headers, tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span>
@ -22873,7 +22873,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
<span id="cb26-373"><a href="#cb26-373" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-374"><a href="#cb26-374" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: tbl-inequality-terms</span></span>
<span id="cb26-375"><a href="#cb26-375" aria-hidden="true" tabindex="-1"></a><span class="co">#| tbl-cap: Inequality term cluster</span></span>
<span id="cb26-376"><a href="#cb26-376" aria-hidden="true" tabindex="-1"></a>terms_inequality <span class="op">=</span> pd.read_csv(<span class="st">&quot;02-data/supplementary/terms_inequality.csv&quot;</span>)</span>
<span id="cb26-376"><a href="#cb26-376" aria-hidden="true" tabindex="-1"></a>terms_inequality <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/terms_inequality.csv&quot;</span>)</span>
<span id="cb26-377"><a href="#cb26-377" aria-hidden="true" tabindex="-1"></a>md(tabulate(terms_inequality.fillna(<span class="st">&quot;&quot;</span>), showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span><span class="st">&quot;keys&quot;</span>, tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span>
<span id="cb26-378"><a href="#cb26-378" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-379"><a href="#cb26-379" aria-hidden="true" tabindex="-1"></a></span>
@ -22897,7 +22897,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
<span id="cb26-399"><a href="#cb26-399" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-400"><a href="#cb26-400" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: tbl-inclusion-criteria</span></span>
<span id="cb26-401"><a href="#cb26-401" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-402"><a href="#cb26-402" aria-hidden="true" tabindex="-1"></a>inclusion_criteria <span class="op">=</span> pd.read_csv(<span class="st">&quot;02-data/supplementary/inclusion-criteria.tsv&quot;</span>, sep<span class="op">=</span><span class="st">&quot;</span><span class="ch">\t</span><span class="st">&quot;</span>)</span>
<span id="cb26-402"><a href="#cb26-402" aria-hidden="true" tabindex="-1"></a>inclusion_criteria <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/inclusion-criteria.tsv&quot;</span>, sep<span class="op">=</span><span class="st">&quot;</span><span class="ch">\t</span><span class="st">&quot;</span>)</span>
<span id="cb26-403"><a href="#cb26-403" aria-hidden="true" tabindex="-1"></a>md(tabulate(inclusion_criteria, showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span><span class="st">&quot;keys&quot;</span>, tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span>
<span id="cb26-404"><a href="#cb26-404" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-405"><a href="#cb26-405" aria-hidden="true" tabindex="-1"></a></span>
@ -22948,7 +22948,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
<span id="cb26-452"><a href="#cb26-452" 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="cb26-453"><a href="#cb26-453" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-454"><a href="#cb26-454" 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="cb26-455"><a href="#cb26-455" aria-hidden="true" tabindex="-1"></a><span class="co"># </span><span class="al">FIXME</span><span class="co"> use 02-data/supplementary undeduplciated counts to get database starting and snowballing counts</span></span>
<span id="cb26-455"><a href="#cb26-455" 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>
<span id="cb26-456"><a href="#cb26-456" aria-hidden="true" tabindex="-1"></a><span class="co"># from: https://github.com/quarto-dev/quarto-cli/discussions/6508</span></span>
<span id="cb26-457"><a href="#cb26-457" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f&quot;&quot;&quot;</span></span>
<span id="cb26-458"><a href="#cb26-458" aria-hidden="true" tabindex="-1"></a><span class="ss">```</span><span class="sc">{</span>mermaid<span class="sc">}</span></span>
@ -23145,7 +23145,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
<span id="cb26-659"><a href="#cb26-659" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> strength_for(val):</span>
<span id="cb26-660"><a href="#cb26-660" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> <span class="bu">list</span>(study_strength_bins.keys())[<span class="bu">list</span>(study_strength_bins.values()).index(val)]</span>
<span id="cb26-661"><a href="#cb26-661" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-662"><a href="#cb26-662" aria-hidden="true" tabindex="-1"></a>findings_institutional <span class="op">=</span> pd.read_csv(<span class="st">&quot;02-data/supplementary/findings-institutional.csv&quot;</span>)</span>
<span id="cb26-662"><a href="#cb26-662" aria-hidden="true" tabindex="-1"></a>findings_institutional <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/findings-institutional.csv&quot;</span>)</span>
<span id="cb26-663"><a href="#cb26-663" aria-hidden="true" tabindex="-1"></a>fd_df <span class="op">=</span> validity.add_to_findings(findings_institutional, by_intervention, study_strength_bins)</span>
<span id="cb26-664"><a href="#cb26-664" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-665"><a href="#cb26-665" aria-hidden="true" tabindex="-1"></a>md(tabulate(fd_df[[<span class="st">&quot;area of policy&quot;</span>, <span class="st">&quot;internal_validity&quot;</span>, <span class="st">&quot;external_validity&quot;</span>, <span class="st">&quot;findings&quot;</span>, <span class="st">&quot;channels&quot;</span>]].fillna(<span class="st">&quot;&quot;</span>), showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span>[<span class="st">&quot;area of policy&quot;</span>, <span class="st">&quot;internal strength&quot;</span>, <span class="st">&quot;external strength&quot;</span>, <span class="st">&quot;main findings&quot;</span>, <span class="st">&quot;channels&quot;</span>], tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span>
@ -23378,7 +23378,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
<span id="cb26-894"><a href="#cb26-894" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: tbl-findings-structural</span></span>
<span id="cb26-895"><a href="#cb26-895" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> src.model <span class="im">import</span> validity</span>
<span id="cb26-896"><a href="#cb26-896" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-897"><a href="#cb26-897" aria-hidden="true" tabindex="-1"></a>findings_structural <span class="op">=</span> pd.read_csv(<span class="st">&quot;02-data/supplementary/findings-structural.csv&quot;</span>)</span>
<span id="cb26-897"><a href="#cb26-897" aria-hidden="true" tabindex="-1"></a>findings_structural <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/findings-structural.csv&quot;</span>)</span>
<span id="cb26-898"><a href="#cb26-898" aria-hidden="true" tabindex="-1"></a>fd_df <span class="op">=</span> validity.add_to_findings(findings_structural, by_intervention, study_strength_bins)</span>
<span id="cb26-899"><a href="#cb26-899" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-900"><a href="#cb26-900" aria-hidden="true" tabindex="-1"></a>md(tabulate(fd_df[[<span class="st">&quot;area of policy&quot;</span>, <span class="st">&quot;internal_validity&quot;</span>, <span class="st">&quot;external_validity&quot;</span>, <span class="st">&quot;findings&quot;</span>, <span class="st">&quot;channels&quot;</span>]].fillna(<span class="st">&quot;&quot;</span>), showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span>[<span class="st">&quot;area of policy&quot;</span>, <span class="st">&quot;internal strength&quot;</span>, <span class="st">&quot;external strength&quot;</span>, <span class="st">&quot;main findings&quot;</span>, <span class="st">&quot;channels&quot;</span>], tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span>
@ -23615,7 +23615,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
<span id="cb26-1133"><a href="#cb26-1133" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: tbl-findings-agency</span></span>
<span id="cb26-1134"><a href="#cb26-1134" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> src.model <span class="im">import</span> validity</span>
<span id="cb26-1135"><a href="#cb26-1135" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1136"><a href="#cb26-1136" aria-hidden="true" tabindex="-1"></a>findings_agency <span class="op">=</span> pd.read_csv(<span class="st">&quot;02-data/supplementary/findings-agency.csv&quot;</span>)</span>
<span id="cb26-1136"><a href="#cb26-1136" aria-hidden="true" tabindex="-1"></a>findings_agency <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/findings-agency.csv&quot;</span>)</span>
<span id="cb26-1137"><a href="#cb26-1137" aria-hidden="true" tabindex="-1"></a>fd_df <span class="op">=</span> validity.add_to_findings(findings_agency, by_intervention, study_strength_bins)</span>
<span id="cb26-1138"><a href="#cb26-1138" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1139"><a href="#cb26-1139" aria-hidden="true" tabindex="-1"></a>md(tabulate(fd_df[[<span class="st">&quot;area of policy&quot;</span>, <span class="st">&quot;internal_validity&quot;</span>, <span class="st">&quot;external_validity&quot;</span>, <span class="st">&quot;findings&quot;</span>, <span class="st">&quot;channels&quot;</span>]].fillna(<span class="st">&quot;&quot;</span>), showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span>[<span class="st">&quot;area of policy&quot;</span>, <span class="st">&quot;internal strength&quot;</span>, <span class="st">&quot;external strength&quot;</span>, <span class="st">&quot;main findings&quot;</span>, <span class="st">&quot;channels&quot;</span>], tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span>

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@ -1,5 +1,5 @@
---
bibliography: 02-data/intermediate/zotero-library.bib
bibliography: data/intermediate/zotero-library.bib
csl: /home/marty/documents/library/utilities/styles/APA-7.csl
papersize: A4
linestretch: 1.5
@ -30,7 +30,7 @@ import bibtexparser
sns.set_style("whitegrid")
DATA_DIR=Path("./02-data")
DATA_DIR=Path("./data")
RAW_DATA=DATA_DIR.joinpath("raw")
WORKING_DATA=DATA_DIR.joinpath("intermediate")
PROCESSED_DATA=DATA_DIR.joinpath("processed")
@ -102,7 +102,7 @@ nr_out_language = len([1 for kw in all_keywords if "out::language" in kw])
nr_extraction_done = len([1 for kw in all_keywords if "done::extracted" in kw])
t3 = "`" * 3
# FIXME use 02-data/supplementary undeduplciated counts to get database starting and snowballing counts
# FIXME use data/supplementary undeduplciated counts to get database starting and snowballing counts
# from: https://github.com/quarto-dev/quarto-cli/discussions/6508
print(f"""
```{{mermaid}}

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@ -1,5 +1,5 @@
---
bibliography: ../02-data/intermediate/zotero-library.bib
bibliography: ../data/intermediate/zotero-library.bib
csl: /home/marty/documents/library/utilities/styles/APA-7.csl
papersize: A4
linestretch: 1.5
@ -22,7 +22,7 @@ subtitle: Addressing inequalities in the World of Work
```{python}
#| echo: false
from pathlib import Path
data_dir=Path("../02-data")
data_dir=Path("../data")
## standard imports
from IPython.core.display import Markdown as md

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@ -1,5 +1,5 @@
---
bibliography: 02-data/intermediate/zotero-library.bib
bibliography: data/intermediate/zotero-library.bib
title: Grab yml
---
@ -187,7 +187,7 @@ import bibtexparser
sns.set_style("whitegrid")
DATA_DIR=Path("./02-data")
DATA_DIR=Path("./data")
RAW_DATA=DATA_DIR.joinpath("raw")
WORKING_DATA=DATA_DIR.joinpath("intermediate")
PROCESSED_DATA=DATA_DIR.joinpath("processed")

View file

@ -15,7 +15,7 @@ import bibtexparser
sns.set_style("whitegrid")
DATA_DIR=Path("./02-data")
DATA_DIR=Path("./data")
RAW_DATA=DATA_DIR.joinpath("raw")
WORKING_DATA=DATA_DIR.joinpath("intermediate")
PROCESSED_DATA=DATA_DIR.joinpath("processed")
@ -120,7 +120,7 @@ datavis:
```{python}
findings_institutional = pd.read_csv("02-data/supplementary/findings-institutional.csv")
findings_institutional = pd.read_csv("data/supplementary/findings-institutional.csv")
findings_institutional
from src.model import validity
import math

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@ -16,7 +16,7 @@ import bibtexparser
sns.set_style("whitegrid")
DATA_DIR=Path("./02-data")
DATA_DIR=Path("./data")
RAW_DATA=DATA_DIR.joinpath("raw")
WORKING_DATA=DATA_DIR.joinpath("intermediate")
PROCESSED_DATA=DATA_DIR.joinpath("processed")

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@ -1,5 +1,5 @@
---
bibliography: 02-data/intermediate/zotero-library.bib
bibliography: data/intermediate/zotero-library.bib
csl: /home/marty/documents/library/utilities/styles/APA-7.csl
papersize: A4
linestretch: 1.5
@ -28,7 +28,7 @@ zotero:
```{python}
#| echo: false
from pathlib import Path
DATA_DIR=Path("./02-data")
DATA_DIR=Path("./data")
BIB_PATH = DATA_DIR.joinpath("raw/01_wos-sample_2023-11-02")
## standard imports

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@ -1,5 +1,5 @@
---
bibliography: 02-data/intermediate/zotero-library.bib
bibliography: data/intermediate/zotero-library.bib
csl: /home/marty/documents/library/utilities/styles/APA-7.csl
papersize: A4
linestretch: 1.5
@ -22,7 +22,7 @@ subtitle: Conceptual Definitions and Key Terms
```{python}
#| echo: false
from pathlib import Path
DATA_DIR=Path("./02-data")
DATA_DIR=Path("./data")
RAW_DATA=DATA_DIR.joinpath("raw")
WORKING_DATA=DATA_DIR.joinpath("intermediate")
PROCESSED_DATA=DATA_DIR.joinpath("processed")
@ -396,7 +396,7 @@ Policy *areas*, identified by @ILO2022b:
#| label: tbl-inclusion-criteria
#| tbl-cap: Study inclusion and exclusion scoping criteria {#tbl-inclusion-criteria}
inclusion_criteria = pd.read_csv("02-data/supplementary/inclusion-criteria.tsv", sep="\t")
inclusion_criteria = pd.read_csv("data/supplementary/inclusion-criteria.tsv", sep="\t")
md(tabulate(inclusion_criteria, showindex=False, headers="keys", tablefmt="grid"))
```

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@ -43,19 +43,19 @@ cmd = "nvim"
[tool.poe.tasks.extract]
help = "Extract the csv data from raw yaml files"
shell = """
python src/extract/raw_to_extracted_csv.py > 02-data/processed/extracted.csv
python src/extract/raw_to_extracted_csv.py > data/processed/extracted.csv
"""
[tool.poe.tasks.prisma]
help = "Update PRISMA flowchart numbers"
shell = """
python src/model/prisma.py > 02-data/processed/prisma.mmd
python src/model/prisma.py > data/processed/prisma.mmd
"""
[tool.poe.tasks.milestone]
help = "Extract, render, commit and version a finished artifact"
shell = """
quarto render --output-dir 05-final_paper
VERSION="$(poetry version -s minor)"
git add pyproject.toml 02-data 05-final_paper
git add pyproject.toml data 05-final_paper
git commit -m "Publish version $VERSION" --no-gpg-sign
git tag -a -m "new bundle for $(date -Isecond)" "$VERSION"
"""

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@ -303,7 +303,7 @@ with the search query requiring a term from the general column and one other col
```{python}
#| label: tbl-wow-terms
#| tbl-cap: World of work term cluster
terms_wow = pd.read_csv("02-data/supplementary/terms_wow.csv")
terms_wow = pd.read_csv("data/supplementary/terms_wow.csv")
Markdown(tabulate(terms_wow.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
```
@ -319,7 +319,7 @@ For the database query, a single term from the general category is required to b
```{python}
#| label: tbl-intervention-terms
#| tbl-cap: Policy intervention term cluster
terms_policy = pd.read_csv("02-data/supplementary/terms_policy.csv")
terms_policy = pd.read_csv("data/supplementary/terms_policy.csv")
# different headers to include 'social norms'
headers = ["General", "Institutional", "Structural", "Agency & social norms"]
Markdown(tabulate(terms_policy.fillna(""), showindex=False, headers=headers, tablefmt="grid"))
@ -331,7 +331,7 @@ as seen in @tbl-inequality-terms.
```{python}
#| label: tbl-inequality-terms
#| tbl-cap: Inequality term cluster
terms_inequality = pd.read_csv("02-data/supplementary/terms_inequality.csv")
terms_inequality = pd.read_csv("data/supplementary/terms_inequality.csv")
Markdown(tabulate(terms_inequality.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
```
@ -355,7 +355,7 @@ with a focus on the narrowing criteria specified in @tbl-inclusion-criteria.
```{python}
#| label: inclusion-criteria
inclusion_criteria = pd.read_csv("02-data/supplementary/inclusion-criteria.tsv", sep="\t")
inclusion_criteria = pd.read_csv("data/supplementary/inclusion-criteria.tsv", sep="\t")
Markdown(tabulate(inclusion_criteria, showindex=False, headers="keys", tablefmt="grid"))
```
@ -382,7 +382,7 @@ The resulting process can be seen in @fig-prisma.
```{mermaid}
%%| label: fig-prisma
%%| fig-cap: PRISMA flowchart for scoping process
%%| file: 02-data/processed/prisma.mmd
%%| file: data/processed/prisma.mmd
```
All relevant data concerning both their major findings and statistical significance are then extracted from the individual studies into a collective results matrix.
@ -559,7 +559,7 @@ study_strength_bins = {
def strength_for(val):
return list(study_strength_bins.keys())[list(study_strength_bins.values()).index(val)]
findings_institutional = pd.read_csv("02-data/supplementary/findings-institutional.csv")
findings_institutional = pd.read_csv("data/supplementary/findings-institutional.csv")
fd_df = validity.add_to_findings(findings_institutional, by_intervention, study_strength_bins)
Markdown(tabulate(fd_df[["area of policy", "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
@ -791,7 +791,7 @@ One limitation of the study is the modelling assumption that workers will have t
#| label: tbl-findings-structural
from src.model import validity
findings_structural = pd.read_csv("02-data/supplementary/findings-structural.csv")
findings_structural = pd.read_csv("data/supplementary/findings-structural.csv")
fd_df = validity.add_to_findings(findings_structural, by_intervention, study_strength_bins)
Markdown(tabulate(fd_df[["area of policy", "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
@ -1028,7 +1028,7 @@ Though the intervention clearly aims at strengthening some aspect of individual
#| label: tbl-findings-agency
from src.model import validity
findings_agency = pd.read_csv("02-data/supplementary/findings-agency.csv")
findings_agency = pd.read_csv("data/supplementary/findings-agency.csv")
fd_df = validity.add_to_findings(findings_agency, by_intervention, study_strength_bins)
Markdown(tabulate(fd_df[["area of policy", "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))

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@ -9,7 +9,7 @@ try:
except ModuleNotFoundError:
import yml as yaml # for directly running the package
DEFAULT_YAML_PATH = Path("02-data/processed")
DEFAULT_YAML_PATH = Path("data/processed")
def to_tsv(studies: list[dict]) -> str:

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@ -3,7 +3,7 @@ import os
PROJECT_DIR=Path(os.getenv("QUARTO_PROJECT_DIR", "."))
DATA_DIR=PROJECT_DIR.joinpath("02-data")
DATA_DIR=PROJECT_DIR.joinpath("data")
RAW_DATA=DATA_DIR.joinpath("raw")
WORKING_DATA=DATA_DIR.joinpath("intermediate")

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@ -33,7 +33,7 @@ del bib_sample, bib_sample_raw_db
if __name__ == "__main__":
nr = PrismaNumbers()
# FIXME use 02-data/supplementary undeduplciated counts to get database starting and snowballing counts
# FIXME use data/supplementary undeduplciated counts to get database starting and snowballing counts
outp = f"""
flowchart TD;
search_db["Records identified through database searching (n={nr.nr_database_query_raw})"] --> starting_sample;