Concurrence électorale et positionnement des partis politiques
Bibliographic record
Abstract
Cet article passe en revue la littérature sur le positionnement des partis politiques dans des espaces uni- et multidimensionnels. Tout au long de ce document, nous faisons l’hypothèse que deux partis s’affrontent dans le cadre d’une élection et s’engagent à tenir leurs promesses électorales une fois élus. Cette étude souligne l’importance de trois hypothèses de modélisation : (i) l’influence du type d’incertitude sur l’issue électorale, (ii) l’objectif des partis politiques (objectif électoraliste – qui consiste à maximiser l’espérance du nombre de voix reçues ou la probabilité de gagner les élections – objectif idéologique – fondé sur les programmes politiques – ou les deux), et (iii) les préférences des électeurs (dans quelle mesure ceux-ci se soucient de l’identité des partis au-delà de la politique mise en place par le vainqueur).
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".