Debating Gun Control in Canada and the United States:<i>Divergent Policy Frames and Political Cultures</i>
Bibliographic record
Abstract
The weakness of the antigun lobby in the United States is attributed to the “collective action problem” of trying to mobilize “free riders” behind a public purpose. But the Coalition for Gun Control emerged in Canada to successfully lobby for the Firearms Act of 1995. If the “collective action problem” is not limited to the United States, then are its effects “mediated” by political culture? To address this research question, we content analyze (1) media coverage, (2) party platforms, (3) presidential, and (4) ministerial rhetoric. Three frames represent “restrictive” gun policies that ban or regulate firearms, “punitive” gun policies that penalize the person for the unlawful use of firearms, or “lenient” gun policies that encourage gun ownership and gun rights. Marked differences in framing the gun debate help explain why an antigun coalition emerged in Canada but not the United States.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".