Do Canadian and U.S. American handgun owners differ?
Bibliographic record
Abstract
This study of male Canadian (n = 475) and U.S. (n = 425) handgun owners addresses 2 questions: (a) Are there differences in gun-related motivation and behaviour patterns; and (b) does the Model of Defensive Gun Ownership of Stroebe, Leander, and Kruglanski (2017) fit data of Canadian handgun gun owners? U.S. and Canadian gun cultures are supposed to be different: Unlike most U.S. gun owners, Canadian gun owners are not assumed to purchase guns for self-defense because they trust their government to protect them against crime. Although Canadian and U.S. handgun owners differed in their gun-related motivation and behaviour patterns, these differences were less substantial than expected: Mean levels of trust in law enforcement of Canadian and U.S. handgun owners did not differ. Furthermore, half of Canadian gun owners considered self-defense to be an important reason for gun ownership. Finally, a structural equation model that had fit the U.S. data of Stroebe et al. (2017) could also be applied to the Canadian data. Given that 30% of all Canadian handguns were purchased between 2012 and 2017, which is when shootings became more common in Canada’s large cities, we speculate that recent events may have reduced differences that might have existed between Canadian and American handgun owners.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".