Bibliographic record
Abstract
Canada has implemented legislation covering all firearms since 1977 and presents a model to examine incremental firearms control. The effect of legislation on homicide by firearm and the subcategory, spousal homicide, is controversial and has not been well studied to date. Legislative effects on homicide and spousal homicide were analyzed using data obtained from Statistics Canada from 1974 to 2008. Three statistical methods were applied to search for any associated effects of firearms legislation. Interrupted time series regression, ARIMA, and Joinpoint analysis were performed. Neither were any significant beneficial associations between firearms legislation and homicide or spousal homicide rates found after the passage of three Acts by the Canadian Parliament--Bill C-51 (1977), C-17 (1991), and C-68 (1995)--nor were effects found after the implementation of licensing in 2001 and the registration of rifles and shotguns in 2003. After the passage of C-68, a decrease in the rate of the decline of homicide by firearm was found by interrupted regression. Joinpoint analysis also found an increasing trend in homicide by firearm rate post the enactment of the licensing portion of C-68. Other factors found to be associated with homicide rates were median age, unemployment, immigration rates, percentage of population in low-income bracket, Gini index of income equality, population per police officer, and incarceration rate. This study failed to demonstrate a beneficial association between legislation and firearm homicide rates between 1974 and 2008.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".