Bibliographic record
Abstract
Canadian homicide clearance rates are higher than for any other type of crime, but clearance rates have been decreasing since the late 1960s and they are not uniform across the country. This article examines homicide clearance in Canada using data derived from the Canadian Homicide Survey to determine whether the evident temporal and geographical variations in clearance are explained by either victim characteristics or offense details. There are two competing theories regarding homicide clearance characteristics. On one hand, it is argued that the police use discretion when determining which cases deserving more attention. The alternate theory is that police apportion the same effort to all homicide cases, regardless of the victim’s status owing to the heinous nature of the crime; therefore, only case details impede the process of determining culpability. Using logistic regression analysis, this examination first focuses on the influence of time and geography on clearance probabilities and then compares the effect of victim characteristics and offense characteristics on clearance outcomes. Empirically nested models indicate that victim characteristics are not a robust predictor of clearance; offense characteristics are found to be more influential. However, both temporal and geographical factors remain important predictors of homicide clearance. The impacts of increasing gang- and drug-related homicides are discussed, as are implications for future research.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".