The Failed Experiment : Gun Control and Public Safety in Canada, Australia, England and Wales
Bibliographic record
Abstract
Widely televised firearm murders in many countries during the 20th Century have spurred politicians to introduce restrictive gun laws. The politicians then promise that the new restrictions will reduce criminal violence and "create a safer society." It is time to pause and ask if gun laws actually do reduce criminal violence. Gun laws must be demonstrated to cut violent crime or gun control is no more than a hollow promise. What makes gun control so compelling for many is the belief that violent crime is driven by the availability of guns and, more importantly, that criminal violence in general may be reduced by limiting access to firearms. In this study, I examine crime trends in Commonwealth countries that have recently introduced firearm regulations: i.e., Great Britain, Australia, and Canada. The widely ignored key to evaluating firearm regulations is to examine trends in total violent crime, not just firearms crime. Since firearms are only a small fraction of criminal violence, the public would not be safer if the new law could reduce firearm violence but had no effect on total criminal violence. The upshot is that violent crime rates, and homicide rates in particular, have been falling in the United States, but increasing in Canada, Australia, and the United Kingdom. The drop in the American crime rate is even more impressive when compared with the rest of the world. In 18 of the 25 countries surveyed by the British Home Office, violent crime increased during the 1990s. This contrast should provoke thinking people to wonder what happened in those countries where they introduced increasingly restrictive firearm laws.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".