Parity Sanctions and Campaign Financing in France: Increased Numbers, Little Concrete Gender Transformation
Bibliographic record
Abstract
This chapter examines how parity sanctions targeted at the major political parties since 2002 have impacted gendered processes of nomination. It also investigates how gender plays a role in individual decisions to seek nomination and run for office with a focus on whether the most recent increase in parity sanctions in the 2017 elections matter for promoting gender equality. While financial sanctions played a role in increasing the number of female candidates, especially for left-wing and small political parties, we argue that the massive increase in women’s presence in the 2017 election cycle is mostly due to other factors, like the radical changes in the political party system in 2017. The chapter first highlights the context for parity reform debates and the implementation set by general campaign financing regulation and electoral reforms. Next, it assesses the relative effects of the financial sanctions as compared to other electoral reforms and other important changes in the political landscape. In the last section, we apply a more micro-sociological analysis to the three constituencies to examine campaign financing and recruitment processes in the 2017 elections. The conclusion discusses the implications of this study for gender equality in political life in France.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".