La Relation Voix-Sieges et les Sources du Paradoxe du Referendum: Cas des Elections Cantonales
Bibliographic record
Abstract
[version préliminaire] Cet article étudie les sources du paradoxe du référendum. Celles-ci ne sont que les biais résultant du transfert des voix en sièges. Ces biais se résument principalement dans les trois facteurs suivants: le gerrymandering, le malap-portionment et le taux de participation auxquels s’ajoutent parfois la question de l’influence d’un tiers parti (victorieux ou non). En employant les données des élections cantonales de la France métropolitaine de 1985 a ̀ 2004, nous cal-culons l’impact de chaque facteurs en adaptant la méthode de Broockes mod-ifiée par Johnston et al. ([24]). Nous déterminons par la suite la source prin-cipale du paradoxe de référendum au niveau de chaque département touché par ce paradoxe. MOTS-CLÉS: Théorie du vote, paradoxe du référendum, élections cantonales, biais.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 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".