The Spouse in the House: What Explains the Marriage Gap in Canada?
Bibliographic record
Abstract
Many traditional vote choice studies have focused on the so-called gen-der gap, which refers to the persistent difference in partisan preferences between men and women ~see, among others, Abzug and Kelber, 1984; Conover, 1988; Mueller, 1991; Chaney, Alvarez and Nagler, 1998!. A less examined phenomenon, but one of perhaps equal consequence, at least for parties of the political right who seek to satisfy core constituen-cies, concerns what has been called the ‘marriage gap. ’ Since it was first identified by Plissner ~1983! in the context of American presidential elec-tions, a small literature has emerged documenting the relationship between marital status and support for conservative parties and candidates in the United States. Perhaps understandably, given the relative paucity of liter-ature even in the American case, there have been no published studies considering the impact of marriage on political attitudes and vote choice in other industrial democracies. As discussed below, hypotheses that seek to explain the marriage
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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 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".