Are young candidates “sacrificial lambs”? Evidence from the 2012, 2017, and 2022 French legislative elections
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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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".