chore(repo): Move manuscripts to separate dir
Both the manuscripts for the working paper and the article are now collected in a separate manuscripts folder.
This commit is contained in:
parent
b4730f6ea8
commit
9fd4a3c791
GPG key ID:
EDBF2ED917B2EF6A
|
|
@ -1,7 +1,7 @@
|
|||
project:
|
||||
type: default
|
||||
render:
|
||||
- article.qmd
|
||||
- manuscript/article.qmd
|
||||
|
||||
format:
|
||||
elsevier-html:
|
||||
|
|
|
|||
|
|
@ -1,9 +1,9 @@
|
|||
project:
|
||||
render:
|
||||
- presentation_summary.md
|
||||
- notes.qmd
|
||||
- meeting_eoy.qmd
|
||||
- scoping_review.qmd
|
||||
- manuscript/presentation_summary.md
|
||||
- manuscript/notes.qmd
|
||||
- manuscript/meeting_eoy.qmd
|
||||
- manuscript/scoping_review.qmd
|
||||
|
||||
toc: true
|
||||
format:
|
||||
|
|
|
|||
|
|
@ -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("data/supplementary/inclusion-criteria.tsv", sep="\t")
|
||||
inclusion_criteria = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/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: 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("data/supplementary/findings-institutional.csv")
|
||||
findings_institutional = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/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("data/supplementary/terms_wow.csv")
|
||||
terms_wow = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/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("data/supplementary/terms_policy.csv")
|
||||
terms_policy = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/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("data/supplementary/terms_inequality.csv")
|
||||
terms_inequality = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/terms_inequality.csv")
|
||||
Markdown(tabulate(terms_inequality.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
|
||||
```
|
||||
|
||||
|
|
@ -1,5 +1,5 @@
|
|||
---
|
||||
bibliography: 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
|
||||
|
|
@ -30,26 +30,22 @@ import bibtexparser
|
|||
|
||||
sns.set_style("whitegrid")
|
||||
|
||||
DATA_DIR=Path("./data")
|
||||
RAW_DATA=DATA_DIR.joinpath("raw")
|
||||
WORKING_DATA=DATA_DIR.joinpath("intermediate")
|
||||
PROCESSED_DATA=DATA_DIR.joinpath("processed")
|
||||
SUPPLEMENTARY_DATA=DATA_DIR.joinpath("supplementary")
|
||||
from src import globals as g
|
||||
|
||||
bib_string=""
|
||||
for partial_bib in RAW_DATA.glob("**/*.bib"):
|
||||
for partial_bib in g.RAW_DATA.glob("**/*.bib"):
|
||||
with open(partial_bib) as f:
|
||||
bib_string+="\n".join(f.readlines())
|
||||
bib_sample_raw_db = bibtexparser.parse_string(bib_string)
|
||||
|
||||
bib_string=""
|
||||
for partial_bib in WORKING_DATA.glob("**/*.bib"):
|
||||
for partial_bib in g.WORKING_DATA.glob("**/*.bib"):
|
||||
with open(partial_bib) as f:
|
||||
bib_string+="\n".join(f.readlines())
|
||||
bib_sample = bibtexparser.parse_string(bib_string)
|
||||
|
||||
# load relevant studies
|
||||
from src import load_data
|
||||
from src.extract import load_data
|
||||
|
||||
# load zotero-based metadata: citations and uses
|
||||
zot_df = pd.DataFrame([
|
||||
|
|
@ -63,10 +59,10 @@ zot_df = pd.DataFrame([
|
|||
], columns = ["doi", "cited", "usage", "keywords"]).drop_duplicates("doi").set_index("doi")
|
||||
|
||||
# Add WB country grouping definitions (income group, world region)
|
||||
WB_COUNTRY_GROUPS_FILE = Path(f"{SUPPLEMENTARY_DATA}/wb-country-groupings.xlsx").resolve()
|
||||
WB_COUNTRY_GROUPS_FILE = Path(f"{g.SUPPLEMENTARY_DATA}/wb-country-groupings.xlsx").resolve()
|
||||
df_country_groups = pd.read_excel(WB_COUNTRY_GROUPS_FILE).set_index("Economy")
|
||||
|
||||
bib_df = (load_data.from_yml(f"{PROCESSED_DATA}/relevant")
|
||||
bib_df = (load_data.from_yml(f"{g.PROCESSED_DATA}/relevant")
|
||||
.assign(
|
||||
doi=lambda _df: _df["uri"].str.extract(r"https?://(?:dx\.)?doi\.org/(.*)", expand=False),
|
||||
zot_cited=lambda _df: _df["doi"].map(zot_df["cited"]),
|
||||
|
|
@ -1,5 +1,5 @@
|
|||
---
|
||||
bibliography: 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
|
||||
|
|
@ -21,12 +21,7 @@ subtitle: Conceptual Definitions and Key Terms
|
|||
|
||||
```{python}
|
||||
#| echo: false
|
||||
from pathlib import Path
|
||||
DATA_DIR=Path("./data")
|
||||
RAW_DATA=DATA_DIR.joinpath("raw")
|
||||
WORKING_DATA=DATA_DIR.joinpath("intermediate")
|
||||
PROCESSED_DATA=DATA_DIR.joinpath("processed")
|
||||
SUPPLEMENTARY_DATA=DATA_DIR.joinpath("supplementary")
|
||||
from src import globals as g
|
||||
|
||||
## standard imports
|
||||
from IPython.core.display import Markdown as md
|
||||
|
|
@ -396,7 +391,7 @@ 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("data/supplementary/inclusion-criteria.tsv", sep="\t")
|
||||
inclusion_criteria = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/inclusion-criteria.tsv", sep="\t")
|
||||
md(tabulate(inclusion_criteria, showindex=False, headers="keys", tablefmt="grid"))
|
||||
```
|
||||
|
||||
|
|
@ -861,7 +856,7 @@ from @Pinto2021:
|
|||
```{python}
|
||||
#| echo: false
|
||||
#| output: asis
|
||||
with open(f"{SUPPLEMENTARY_DATA}/query.txt") as f:
|
||||
with open(f"{g.SUPPLEMENTARY_DATA}/query.txt") as f:
|
||||
query = f.read()
|
||||
|
||||
t3 = "`" * 3
|
||||
|
|
@ -26,6 +26,7 @@ crossref:
|
|||
#| echo: false
|
||||
#| output: false
|
||||
import src.globals as g
|
||||
import re
|
||||
from IPython.display import display, Markdown, HTML
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
|
@ -303,7 +304,7 @@ 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("data/supplementary/terms_wow.csv")
|
||||
terms_wow = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/terms_wow.csv")
|
||||
Markdown(tabulate(terms_wow.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
|
||||
```
|
||||
|
||||
|
|
@ -319,7 +320,7 @@ 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("data/supplementary/terms_policy.csv")
|
||||
terms_policy = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/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 +332,7 @@ as seen in @tbl-inequality-terms.
|
|||
```{python}
|
||||
#| label: tbl-inequality-terms
|
||||
#| tbl-cap: Inequality term cluster
|
||||
terms_inequality = pd.read_csv("data/supplementary/terms_inequality.csv")
|
||||
terms_inequality = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/terms_inequality.csv")
|
||||
Markdown(tabulate(terms_inequality.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
|
||||
```
|
||||
|
||||
|
|
@ -355,7 +356,7 @@ with a focus on the narrowing criteria specified in @tbl-inclusion-criteria.
|
|||
```{python}
|
||||
#| label: inclusion-criteria
|
||||
|
||||
inclusion_criteria = pd.read_csv("data/supplementary/inclusion-criteria.tsv", sep="\t")
|
||||
inclusion_criteria = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/inclusion-criteria.tsv", sep="\t")
|
||||
Markdown(tabulate(inclusion_criteria, showindex=False, headers="keys", tablefmt="grid"))
|
||||
```
|
||||
|
||||
|
|
@ -382,7 +383,7 @@ The resulting process can be seen in @fig-prisma.
|
|||
```{mermaid}
|
||||
%%| label: fig-prisma
|
||||
%%| fig-cap: PRISMA flowchart for scoping process
|
||||
%%| file: 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 +560,7 @@ 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("data/supplementary/findings-institutional.csv")
|
||||
findings_institutional = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/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 +792,7 @@ 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("data/supplementary/findings-structural.csv")
|
||||
findings_structural = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/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 +1029,7 @@ 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("data/supplementary/findings-agency.csv")
|
||||
findings_agency = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/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"))
|
||||
Loading…
Reference in a new issue