Bibliographic record
Abstract
Abstract.As compared with federal and provincial elections, municipal elections in Canada present voters with challenges of informational quantity and quality. These unique challenges have implications for the psychological structure of citizens' voting calculus. Using a survey of voters conducted after the city of Vancouver civic election of 2002, we estimate a model of vote choice for mayor. We show that voters respond to the different context in predictable ways: their choices are determined largely by ideological orientations and provincial partisanship, with local economic evaluations and local issues playing only a very small role. Résumé.Si on les compare aux élections fédérales et provinciales, les élections municipales au Canada posent, pour les électeurs, des problèmes d'accès à une information de qualité en quantité suffisante. Ces défis particuliers ont un impact sur la structure psychologique de leurs stratégies de vote. Nous estimerons ici un modèle de choix d'un candidat au poste de maire à l'aide des données d'un sondage effectué après les élections municipales de Vancouver de 2002. Nous démontrerons que les électeurs répondent à ce contexte différent de manière prévisible : leurs choix sont déterminés principalement par leur orientation idéologique et leur soutien partisan au niveau provincial, alors que l'évaluation de l'état de l'économie locale et les questions de politique locale ne jouent qu'un rôle limité.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".