Questioning Canadian Criminal Incidence Rates: A Re-analysis of the 2004 Canadian Victimization Survey
Bibliographic record
Abstract
This article re-analyses official 2004 criminal incidence rates in Canada. Currently, official incidence rates are calculated using a technique known as capping, meaning that any respondent can represent a maximum of three incidents per crime type, regardless of how many incidents the individual reports. Given that research on other victimization surveys has cast doubt on the practice of capping, this research assesses the effects of capping in the Canadian Victimization Survey. Findings illustrate that there is significant cause to question the way in which official incidence rates are calculated. Specifically, this research shows that violent crime increases by 87% and household crime increases by 36% when all reported incidents are included. This pattern not only underscores the importance of understanding how incidence rates are produced but also suggests that capping may ignore genuine incidents because individuals who are victims of violent crimes are the most likely to be repeatedly victimized. These findings indicate numerous rates should be published, and more research needs to be conducted to understand recall in victimization surveys and determine the most accurate methods for incidence rate estimation.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.029 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 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".