An Evaluation of the 1977 Canadian Firearm Legislation: Robbery Involving a Firearm.” Applied Economics 35
Bibliographic record
Abstract
This article uses a pooled cross-section, time-series model to evaluate the effect of the 1977 Canadian firearms legislation on the provincial homicide rate between 1969 and 1989. This type of model was selected because of its ability to capture variation across space as well as time. The indices included in this model, measured at the provincial level, as independent variables are: unemployment rate, percentage Status Indian, percentage immigrant, percentage male youth, the clearance rate. The results are consistent with the findings of most previous studies that the 1977 Canadian firearms legislation did not have a significant effect on homicide rates. The strongest explanatory factors were percentage Status Indian and male youth. n 1977, Canada amended the criminal code in an effort to toughen ~t its gun control legislation. This amendment required firearms pur-chasers to apply for a Firearms Acquisition Certificate, strengthened the registration requirements for handguns and other &dquo;restricted weapons,&dquo; and prohibited a variety of weapons. In addition, this amendment increased the penalties for anyone convicted of firearms misuse. At the time of its passage, supporters of this bill voiced high expectations that the bill would reduce firearms deaths; in other words, it was claimed that this gun control measure would not only reduce criminal violence, but it would also reduce accidental AUTHORS ’ NOTE: An earlier draft of this article was presented to the meetings of theAmerican Society of Criminology, San Francisco, November 20-23,1991. This study was supported by the British Columbia Provincial Government’s Challenge ’89 and ’90 programs. The opinions and points of view expressed here are our own and do not necessarily reflect the official positions or any sponsoring agency. The authors would like to thank Karim Gulamhusein for his painstaking and careful work in collecting and assembling the data set. We would particularly like to thank Walter Piovesan of the SFU Library, for his help with the Canadian Justice data tapes. Finally, thanks are due to Gordon MacKay of the Canadian Centre for Justice Statistics, Diane Fournier
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".