Bibliographic record
Abstract
Despite the popularity of the Evidence-Based Policy Making paradigm, scholarly evidence often fails to have an impact in emotional or value-laden policy debates. Consequently, changes to Canada’s gun control laws in recent years have often failed to incorporate scholarly research. This is problematic given that the forces of path dependence impose costs on policy makers who seek to reverse established policies, even if they are dysfunctional. This article lays the theoretical foundations for a Firearms Policy Evaluation Framework, which can be used by scholars, policy makers, advocates, and the public to conduct preliminary evaluations of proposed firearms policies before they become law. The utility of the framework is then demonstrated with an evaluation of the 2020 assault-style weapons ban in Canada, which includes a systematic scoping review of the literature on the impact of assault-weapons bans.
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 | 0.032 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.050 | 0.022 |
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".