The Patterns and Influences of Women's Legislative in Simultaneously General Elections in Indonesia
Bibliographic record
Abstract
Gender is one of the essential aspects of the democratic process, including in the legislative elections. In this regard, this study aims to see the implementation of recruitment patterns and the influences used by women, and the factors that win women in Indonesia's legislative elections. This research is qualitative research by carrying out the documentation at the General Election Commission Office and in-depth interviews with women legislators elected in the 2019 general elections. The results showed that the Indonesian government has implemented affirmative action well, indicated by a minimum quota of 30% for female legislators. Besides, affirmative action's success is also supported by recruiting legislative candidates by parties using several approaches, namely the oligarchic approach, the cadre selection approach, the structural approach, the transparency approach, and the dedicated approach. Furthermore, the prevalence and factors that support women legislators' success in the 2019 elections, which have increased compared to the 2014 elections, influence the sequence number and the incumbent candidates' influence. In the end, this affirmative action policy positively affects women who want to take part in politics.
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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 | 0.000 | 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 | 0.001 |
| 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".