Bibliographic record
Abstract
In the last two decades the number of women involved in politics locally and nationally has increased.  Nevertheless, there is limited empirical work investigating the increase in the number of female candidates for the position of mayor. To fill this gap in the literature, we conducted interviews with 57 of the 72 female candidates for mayor in Israel before the October 2018 elections, and 37 of the 72 female candidates for mayor after the election. In addition, we interviewed 11 male candidates and men elected as mayors after the election, as well. On the individual level with regard to political ambition, we found that there are four components whose synergy results in more women being encouraged to run for mayor: mentoring, information, networking for women and training. We called this model the MINT model, which has emerged from the interviews conducted with the candidates. On the societal level, it is important to increase public awareness of the importance of gender representation and hence, voting for women to be mayors.
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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 | 0.003 | 0.003 |
| 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 | 0.000 |
| 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".