'Horizontal' and 'Vertical' Venue Selection: LGBT Rights and Abortion Policy in Canada and the United States
Bibliographic record
Abstract
When pursuing policy goals within formal government structures, interest groups are faced with several choices related to how best to go about targeting and influencing policy makers. One of the most important among these decisions is, within what venue should these efforts proceed? Does focusing mobilization efforts through the Courts or through the legislative branch, a sort of 'horizontal' venue selection, influence movement success or strategies? Does the choice between state-level or federal-level, or 'vertical' venue selection, make a difference? With reference to both the agenda setting literature found in the work of Kingdon (2003), Bachrach and Baratz (1970) and others, as well as legal mobilization theories found in the law and society literature, this study attempts to address these questions in an effort to provide a better understanding of how and why venue choices matter for social movements. Specifically, four cases will be considered, including the fight for same-sex marriage in Canada, efforts to repeal sodomy laws in the United States, the legalization of abortion in Canada, and abortion policy in the United States. Tentative results suggest that both different institutional structures found in each country, as well as differences across particular issue areas, play a profoundly important role in how venue selection ultimately influences policy outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".