Les mesures d’importance de la religion dans les études électorales : une revue de la littérature
Bibliographic record
Abstract
This article seeks to identify the main characteristics of electoral studies, using a religious salience measure, and to test several plausible explanations for the variation in the reported effect of religious salience on electoral behaviour. It builds upon an original dataset that contains 244 articles on the topic published in social sciences journals between 1956 and 2012. Variation in the reported effect of religious salience on electoral behaviour is documented and traced back to different ways of linking orientation to action at the voter level. A few electoral studies are chosen to exemplify the challenges met, while others are probed to help envision challenges ahead. Among these is the need for more relevant measures of the potential impact of religion on electoral behaviour at the voter level.
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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 | 0.048 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".