Towards Solving the Political Gender Imbalance Puzzle: A Mixed Methods Analysis of Parity in France
Bibliographic record
Abstract
This paper takes a mixed methods approach to address the puzzle of the persistence of gender imbalance in political recruitment and elections in the case of French parity sanctions in the National Assembly Elections from 2002 to 2017. Based on a “concurrent nested strategy” (Cresswell 2003), we use a national level qualitative analysis of the candidate selection process and the implementation of parity sanctions against political parties; quantitative analyses of the socio-economic profiles of French representatives and candidates in the National Assembly in relation to their political party affiliation; and field-work in three legislatives constituencies, two in Paris and one in Burgundy on the candidate selection process for the 2017 legislative elections to identify gendered time and money constraints. The study shows the limits of parity reform, the resistance of established gendered practices in political parties at all levels and the value of using mixed methods analysis for solving the puzzle of gender imbalance in political representation and for the study of the Comparative Politics of Gender more broadly speaking.
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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 | 0.079 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".