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+load data, boilerplate:
+
+```{python}
+#| echo: false
+from pathlib import Path
+import re
+## standard imports
+from IPython.core.display import Markdown as md
+import numpy as np
+import pandas as pd
+from matplotlib import pyplot as plt
+import seaborn as sns
+from tabulate import tabulate
+import bibtexparser
+
+sns.set_style("whitegrid")
+
+DATA_DIR=Path("./02-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")
+
+bib_string=""
+for partial_bib in 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"):
+    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
+
+# load zotero-based metadata: citations and uses
+zot_df = pd.DataFrame([
+    [
+        entry["doi"] if "doi" in entry.fields_dict else None,
+        entry["times-cited"] if "times-cited" in entry.fields_dict else None,
+        entry["usage"] if "usage" in entry.fields_dict else None,
+        entry["keywords"] if "keywords" in entry.fields_dict else None,
+    ]
+    for entry in bib_sample.entries
+], 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()
+df_country_groups = pd.read_excel(WB_COUNTRY_GROUPS_FILE).set_index("Economy")
+
+bib_df = (load_data.from_yml(f"{PROCESSED_DATA}")
+    .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"]),
+        zot_usage=lambda _df: _df["doi"].map(zot_df["usage"]),
+        zot_keywords=lambda _df: _df["doi"].map(zot_df["keywords"]),
+        date = lambda _df: pd.to_datetime(_df["year"], format="%Y"),
+        year = lambda _df: _df["date"].dt.year,
+        region = lambda _df: _df["country"].map(df_country_groups["Region"]),
+        income_group = lambda _df: _df["country"].map(df_country_groups["Income group"]),
+    )
+    .query("year >= 2000")
+)
+zot_df = None
+df_country_groups = None
+```
+
+```{python}
+df_country_groups = pd.concat([pd.read_excel(WB_COUNTRY_GROUPS_FILE), pd.DataFrame(data={'Economy':['global'],'Code':['WLD'],'Region':['Europe & Central Asia;South Asia;North America;East Asia & Pacific;Sub-Saharan Africa;Europe & Central Asia;Latin America & Caribbean'], 'Income group':[''], 'Lending category':['']})]).set_index("Economy")
+
+def countries_to_regions(countries:str):
+    res = set()
+    for c in countries.replace(" ;", ";").replace("; ",";").split(";"):
+        if c in df_country_groups.index:
+            region = df_country_groups.at[c,'Region']
+            res.add(region)
+    return ";".join(res)
+
+# countries_to_regions("India; Nicaragua")
+bib_df['region'] = bib_df['country'].map(countries_to_regions)
+bib_df['region'].value_counts().plot.bar()
+```
+
+```{python}
+#| label: fig-intervention-types
+#| fig-cap: Predominant type of intervention
+
+by_intervention = (
+    bib_df.groupby(["author", "year", "title", "design", "method", "representativeness", "citation"])
+    .agg(
+        {
+            "intervention": lambda _col: "; ".join(_col),
+        }
+    )
+    .reset_index()
+    .drop_duplicates()
+    .assign(
+        intervention=lambda _df: _df["intervention"].apply(
+            lambda _cell: set([x.strip() for x in re.sub(r"\(.*\)", "", _cell).split(";")])
+        ),
+    )
+    .explode("intervention")
+)
+sort_order = by_intervention["intervention"].value_counts().index
+
+fig = plt.figure()
+fig.set_size_inches(6, 3)
+ax = sns.countplot(by_intervention, x="intervention", order=by_intervention["intervention"].value_counts().index)
+plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
+         rotation_mode="anchor")
+plt.show()
+```
+
+datavis:
+
+
+```{python}
+findings_institutional = pd.read_csv("02-data/supplementary/findings-institutional.csv")
+findings_institutional
+from src.model import validity
+import math
+
+validities = validity.calculate(by_intervention)
+valid_subset = validities[["internal_validity", "external_validity", "citation"]].fillna(1.0).drop_duplicates(subset=["citation"]).sort_values("internal_validity")
+def combined_validities(df_in, column: str = "internal_validity"):
+    if not isinstance(df_in, str):
+        return
+    combined = 0.0
+    for study in df_in.split(";"):
+        new = valid_subset.loc[valid_subset["citation"] == study, column]
+        if len(new) > 0 and not math.isnan(new.iat[0]):
+            combined += new.iat[0]
+    if combined:
+        return combined
+    return 0.0
+def combined_external(df_in, column: str = "external_validity"):
+    return combined_validities(df_in, column)
+
+findings_institutional["internal_validity"] = findings_institutional["studies"].apply(combined_validities)
+findings_institutional["external_validity"] = findings_institutional["studies"].apply(combined_external)
+findings_institutional[["area of policy",  "internal_validity", "external_validity", "findings", "channels"]]
+```
+
+