Parity Sanctions and Campaign Financing in France: Increased Numbers, Little Concrete Gender Transformation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| 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 it