Does the level of descriptive representation of women have any consequences for policy spending?
Bibliographic record
Abstract
This study examines the potential link between descriptive and substantive representation. More precisely, we examine whether a higher level of political descriptive representation of women improves their substantive representation in terms of policy spending in areas that are known to be prioritized by women. We use data from a pooled sample of all of the 290 Swedish municipalities covering the years from 1994 to 2021. We make at least four contributions to the research field: we use multiple measures of (1) women’s political representation and (2) policy spending, and we also (3) test assumptions at the subnational level, where policy spending matters most, and (4) assess them over a longer period of time, stretching across almost three decades. In contrast to our expectations, the findings show that the descriptive representation of women has no influence on policy spending; instead, economic and demographical aspects dominate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".