Are young candidates “sacrificial lambs”? Evidence from the 2012, 2017, and 2022 French legislative elections
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
The underrepresentation of young adults is widespread in the parliaments of Western democracies. Yet evidence suggests that voters do not have a negative bias towards young candidates. In this article, we focus on another factor that may contribute to youth underrepresentation: the level of competitiveness in districts where political parties nominate young people. Using data on all candidates who ran for a major political party/coalition in the 2012, 2017, and 2022 French legislative elections, we attempt to determine whether young adults tend to be nominated in districts where they have little or no chance of winning. To do so, we use three different measures of district competitiveness. Our results show that young people – and especially young women – are more likely than others to be “sacrificial lambs”. Our analyses nevertheless indicate that men aged between 31 and 35 have become almost as competitive as older people in 2022.
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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 | 0.001 | 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