Social Cleavages, Attitudes and Voting Patterns: A Comparison of Canada and Great Britain
Bibliographic record
Abstract
This paper develops a novel method for compa ative research on social cleavages that integrates the three major approaches to voting behaviour—th soc logical approach, rational choice theory and the party identification model—u der a single theoretical paradigm. We apply this integrated theory to the major regions of the USA, Canada and Great Britain. We find striking national and regional similarities in the effects of social group memberships on attitudes, but considerable diversity in the effects of social group membership on vote. Race is notable exception—it deviates in the absence of uniformity of its effects of the cleavage on both attitudes and vote. That we find significant regional differences within countries suggests the importance of using region, rather than country, as the uni of a alysis. These findings also underline the importance of paying close attention to political context when assessing the effects of social groups on voting.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".