On The Same Page? Support for Gender Quotas among Indonesian Lawmakers
Bibliographic record
Abstract
As a strategy to improve women’s share in Indonesian parliament, gender quotas were introduced in 2002 and first implemented in the 2004 elections. Despite vast research on the influence of gender quotas in nominating women into parliament, little is known about male and female politicians’ acceptance and perception of gender quotas. This paper seeks to explore how distinct are male and female MPs in perceiving gender quotas and in explaining the roots of women’s political under-representation. Using a questionnaire involving 104 representatives (54 male and 50 female), the study suggests a significant gender gap occurs not only in perceptions related to quotas’ positive-discrimination legitimacy and efficiency but also in explanations that hinder women’s electoral success and which strategies might work best in overcoming the disparity. These distinctions matter because they offer insights as to the dynamics explaining why gender quotas are not resulting in a notable increase in women’ parliamentary representation.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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; both teacher heads agree on what is shown here.
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".