chore(script): Add descriptive labels to all compute cells

scoping_review.qmd

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