chore(script): Add descriptive labels to all compute cells
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1 changed files with 9 additions and 2 deletions
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@ -14,6 +14,7 @@ subtitle: Scoping Review on 'What Works'
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---
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```{python}
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#| label: load-data
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#| echo: false
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from pathlib import Path
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import re
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@ -45,9 +46,7 @@ for partial_bib in WORKING_DATA.glob("**/*.bib"):
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with open(partial_bib) as f:
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bib_string+="\n".join(f.readlines())
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bib_sample = bibtexparser.parse_string(bib_string)
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```
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```{python}
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# load relevant studies
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from src import load_data
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@ -424,6 +423,7 @@ they will in turn be crawled for cited sources in a 'snowballing' process,
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and the sources will be added to the sample to undergo the same screening process explained above.
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```{python}
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#| label: calculate-scoping-flowchart
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#| echo: false
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#| output: asis
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@ -472,6 +472,7 @@ The results to be identified in the matrix include a study’s: i) key outcome m
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## Data
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```{python}
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#| label: calculate-relevant-studies
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#| echo: false
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# TODO Remove redundant 'relevant' studies observation below once all studies are extracted.
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nr_relevant = len([1 for kw in all_keywords if "relevant" in kw])
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@ -611,6 +612,7 @@ and the reviewed studies discussed from a perspective of their analysed inequali
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to better identify areas of strong analytical lenses or areas of more limited analyses.
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```{python}
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#| label: fig-validity
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from src import prep_data
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validities = prep_data.calculate_validities(by_intervention)
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@ -631,6 +633,7 @@ g = sns.PairGrid(validities[["internal_validity", "external_validity", "identifi
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::: {#tbl-findings-institutional}
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```{python}
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#| label: tbl-findings-institutional
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from src.model import strength_of_findings as findings
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findings_institutional = pd.read_csv("02-data/supplementary/findings-institutional.csv")
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@ -861,6 +864,7 @@ One limitation of the study is the modelling assumption that workers will have t
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::: {#tbl-findings-structural}
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```{python}
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#| label: tbl-findings-structural
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from src.model import strength_of_findings as findings
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findings_structural = pd.read_csv("02-data/supplementary/findings-structural.csv")
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@ -1095,6 +1099,7 @@ Though the intervention clearly aims at strengthening some aspect of individual
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::: {#tbl-findings-agency}
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```{python}
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#| label: tbl-findings-agency
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from src.model import strength_of_findings as findings
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findings_agency = pd.read_csv("02-data/supplementary/findings-agency.csv")
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@ -1186,6 +1191,7 @@ The authors suggest the primary channel is the newly increased bargaining power
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# Discussion & policy implications
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```{python}
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#| label: prep-inequalities-crosstabs
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# dataframe containing each intervention inequality pair
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df_inequality = (
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bib_df[["region", "intervention", "inequality"]]
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@ -1651,6 +1657,7 @@ while relying on indicators for measurement which are flexible yet overlapping e
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## Full search query
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```{python}
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#| label: full-search-query
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#| echo: false
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#| output: asis
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with open(f"{SUPPLEMENTARY_DATA}/query.txt") as f:
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