What Do We Know About Firearms in Canada?: A Systematic Scoping Review
Bibliographic record
Abstract
Justin Trudeau, the Prime Minister of Canada, recently called for an examination on firearm legislation and for research evidence on best practices to curb gun violence. He also publicly discussed the need to look at the best available evidence to make decisions about firearms in Canada. In light of this, a systematic review of Canadian firearms research was initially attempted; however, searches yielded few results. Given this fruitless finding, a systematic scoping review was conducted of all peer-reviewed, empirical research on firearms in Canada from January 2000 to December 2018 to determine what is the nature and scope of the firearms literature in Canada, as well as what the research findings indicate. Results of the review revealed that the overall volume of peer-reviewed, empirical literature produced during this 18-year period was exceptionally low. In addition, we found significant gaps in the literature, which can impede any future 'evidence-based' approach to firearms in Canada. We discuss these gaps and propose directions for future research to produce better informed Canadian gun policy.
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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 | 0.032 | 0.144 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.042 | 0.057 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".