The profile of gender equality issue attention in Western Europe
Bibliographic record
Abstract
Abstract This article investigates the factors that drive governments to pay attention to gender equality issues and place them upon executive agendas. In line with studies of the dynamics of issue attention, which demonstrate the importance of investigating variability in the attention policy makers give to issue demands across policy domains, this article argues that policy issues related to gender equality are multidimensional and patterns in executive attention vary across the different types of gender issues. Multidimensionality of gender equality issues reflects different dynamics in agenda‐setting as different issues invoke contrasting constellations of political representation, institutional friction and veto points. To investigate this variation, this article proposes a twofold distinction between class‐based and status‐based gender equality issues and assesses the validity of three sets of explanations for when gender issues succeed in reaching executive agendas: women in politics, party ideology and economic performance. Drawing on governmental attention datasets from the Comparative Agendas Project, a systematic comparative quantitative analysis of the determinants of gender equality issue attention in five Western European countries is conducted. The main findings confirm that the mechanisms through which different types of gender equality issues gain executive attention differ according to the kind of the gender equality demand. Costly class‐based gender equality issues are more likely to receive executive attention when the economy is performing well, when there is a strong presence of Social Democrats and when there is a high proportion of female MPs. In contrast, economic performance, party politics and women's parliamentary presence do not seem to exert any impact on status‐based issues. Instead, critical actors in the government seem to be the strongest driver for attention over this second type of gender equality issue. This study contributes a gendered dimension to the policy agendas scholarship, adding theoretical and empirical depth to the understanding of how non‐core issues secure their place on full governmental agendas. By focusing on how to secure governmental attention for gender equality issues, the article makes a major contribution to understanding the initial genesis of gender equality policies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".