Institutional opportunities and party position change: the case of LGBTQ+ rights in Canada
Bibliographic record
Abstract
How does the institutional context shape how much party position change on LGBTQ+ rights is attributable to conversion (i.e., incumbents changing their positions) versus replacement (i.e., legislative turnover)? Research on party position change on LGBTQ+ rights has focused on the US, meaning findings may be limited to a particular institutional context. Turning to the Canadian case, I find that party position change on LGBTQ+ rights was larger in magnitude and happened faster in Canada than in the US. I argue that institutional differences created opportunities for more considerable and rapid conversion and replacement in Canada compared to the US. Institutional opportunities for conversion and replacement can vary by country, by party, and over time. As work on conversion, replacement, and party position change expands further beyond the US, it will be important to consider how the institutional context shapes opportunities for – and the dynamics of – party position change.
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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 | 0.001 | 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".