The Social Costs of Gun Ownership: Gun Control Policy and Crime
Bibliographic record
Abstract
This paper investigates the impact of gun control legislation on the homicide rate across two countries -the US and Canada.The Brady Act from the US creates the national background check system we use today, while the Firearms Act from Canada starts a new national background check system along with labeling certain guns as "restricted" or "banned" for civilian purchase 12 .For each of these policies, I created a regression that predicts the state's or province's homicide rate based on the policy, year, alcohol consumption, police per capita, and many demographic variables to measure the policy's short-term and long-term impact on the firearm homicide rate.Based on these regressions, both acts significantly decreased firearm homicides in the long-term, but the Brady Act had a bigger impact in relation to the current firearm homicide rate at the time.Therefore, the Brady Act was more effective than the Firearms Act.One main future direction with this research is to analyze homicides caused by other means during the same timeframe.Additionally, since firearm homicides significantly decreased with these acts, it would be valuable to know if firearm suicides also decreased. II.1 "Brady Law | Bureau of Alcohol, Tobacco, Firearms and Explosives," accessed May 5, 2021,
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".