feat(script): Add structural findings table
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@ -530,6 +530,54 @@ but in turn increases overall labour force participation and employment.[^ciepli
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## Structural factors
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{{< portrait >}}
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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 validity
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from src.model.validity import strength_for # Careful: ruff org imports will remove
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findings_structural = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/findings-structural.csv")
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fd_df = validity.add_to_findings(findings_structural, df_by_intervention)
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outp = Markdown(
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tabulate(
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fd_df[
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[
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"area of policy",
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"internal_validity",
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"external_validity",
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"findings",
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"channels",
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]
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].fillna(""),
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showindex=False,
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headers=[
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"area of policy",
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"internal strength",
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"external strength",
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"main findings",
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"channels",
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],
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tablefmt="grid",
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)
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)
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del findings_structural, fd_df
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outp # type: ignore[ReportUnusedExpression]
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```
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Note: Each main finding is presented with an internal strength of evidence and an external strength of evidence which describe the combined validities of the evidence base for the respective finding.
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Validities are segmented to a weak (-) evidence base under a validity ranking of `{python} strength_for(r"\+")`,
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evidential (+) from `{python} strength_for(r"\+")` and under `{python} strength_for(r"\++")` and strong evidence base (++) for `{python} strength_for(r"\++")` and above.
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Summary of main findings for structural policies
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:::
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{{< landscape >}}
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## Agency factors
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# Robustness of evidence
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