Policy Gridlock versus Policy Shift in Gun Politics: <i>A Comparative Veto Player Analysis of Gun Control Policies in the United States and Canada</i>
Bibliographic record
Abstract
Why do major events of gun violence (i.e., mass shootings) lead to incremental change or no federal legislative change at all in the United States while major events of gun violence have resulted in large-scale legislative changes in Canada? Exploring the complexities involved in this compelling question, this article conducts a comparative analysis of recent gun control policy gridlock and shift in these two countries. We concentrate on two mass shooting cases in each country: the Columbine (1990) and Sandy Hook (2012) massacres in the United States and the École Polytechnique Massacre (1989) and Concordia Shooting (1992) in Canada. We use veto player theory to gain insights into why tightening gun policy is so difficult to implement in the United States while Canada often follows up with policy transformations after a focusing event. This theory informs the central argument that the key factors underpinning the divergent policy outcomes on gun control issues in both countries involve differences in the structure of government/institutional design and the role and power of interest groups in each case.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".