Bibliographic record
Abstract
Abstract. Do differences in levels of political information affect the vote calculus? Do differences in the decision process along informational divides affect vote choice? Using data from the 2004 Canadian Election Study this research tests the influence of political information on both the vote decision process and incumbent vote shares through a series of analyses that compare actual and simulated behaviour across information levels. The proposition being tested contends that information heterogeneity produces differences in the vote calculus that in turn lead to systematic and significant variation in vote choice. The results suggest that information does indeed affect the decision calculus and outcome, but not necessarily as one might expect. Résumé. Le niveau d'information politique des électeurs a-t-il une incidence sur leur vote? Les différences dans le processus décisionnel associées au niveau d'information influent-elles sur les électeurs? Grâce aux données tirées de l'édition 2004 de l'Étude électorale canadienne et à une série d'estimations et de simulations statistiques, cet article propose de tester l'influence du niveau d'information politique sur le processus décisionnel des électeurs et sur le soutien accordé aux urnes au parti sortant. La proposition testée ici stipule que la présence d'hétérogénéité dans l'information politique des électeurs influe sur leurs mécanismes de décision, ce qui entraîne une variation systématique et significative dans le choix du vote. Les résultats suggèrent que l'information politique a une incidence sur le processus décisionnel des électeurs et sur leur vote, bien que cet impact n'aille pas nécessairement dans le sens attendu.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".