Bibliographic record
Abstract
Canada implemented a series of laws regulating firearms including background checks and licensing, references, psychological questionnaires, prohibition of paramilitary style rifles, and magazine capacity restrictions in order to decrease the incidences and deaths from mass shootings. The associated effects of these laws were examined over the years 1974 to 2020. A model was constructed using difference-in-differences analysis of firearms and non-firearms mass homicide incidences and death rates. Mass homicides were defined as a homicide due to one event involving three or more deaths. Incidence rates of mass homicide by firearm were found to be 0.11 (95%CI 0.08, 0.14) per million compared to a non-firearm mass homicide rate of 0.12 (95% CI 0.10, 0.15) per million. Mass homicide death rates by firearm were found to be 0.39 (95% CI 0.29, 0.49) per million compared to a non-firearm mass homicide rate of 0.47 (95% CI 0.34, 0.61) per million. Overall, there is a gradual declining trend in the incidence of mass homicide by firearm (IRR 0.97 (95% CI 0.96, 0.98)) and by non-firearm (IRR 0.97 (95% CI 0.97, 0.98)). The decline in mass homicide death rate by firearm and non-firearm is IRR 0.96 (95% CI 0.95, 0.97), and IRR 0.97 (95% CI 0.96, 0.98) respectively. No specific associated decrease in mass homicide incidence rates or death rates with firearms legislation was found after the implementation of background checks and prohibition of full auto firearms in 1980, by the implementation of references and psychological questionnaires in 1994, by the restriction of magazine capacity in 1994, the prohibition of paramilitary rifles in 1994, or licensing in 2001.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".