Conventional Calculations of Homicide Rates Lead to an Inaccurate Reflection of Canadian Trends
Bibliographic record
Abstract
Selon les statistiques officielles, le taux d'homicide au Canada est en baisse constante depuis le milieu des années soixante-dix. Or, le taux d'homicide, tel qu'il est calculé normalement, ne traduit pas avec exactitude la véritable nature des homicides. Donc, si on veut utiliser ce taux en tant que mesure sociétale, il faudra l'assortir d'un nouveau taux d'homicide établi à partir des données démographiques sur les caractéristiques des contrevenants. L'auteur exploite de nouvelles techniques statistiques en vue de démontrer que l'évolution du taux d'homicide ajusté en fonction de l'âge se distingue nettement de celle du taux classique: (1) l'évolution du taux n'affiche aucune rupture structurelle avant la fin des années quatre-vingt; (2) le taux est en hausse jusqu'au début des années quatre-vingt-dix; (3) si le taux d'homicide est en baisse depuis le milieu des années quatre-vingt-dix, la quantité de données statistiques ne permet pas toutefois de conclure que la tendance soit de nouveau en baisse. En outre, les résultats de la recherche portent à croire que seules les études démographiques et l'analyse des séries chronologiques permettraient une évaluation exacte de l'évolution des taux d'homicide: en effet, il serait ainsi possible de bien cerner les variables sociales et donc de déterminer avec une plus grande précision leurs effets sur les taux d'homicide. Or, on sait que les taux d'homicide exercent une influence importante sur l'opinion publique et sur les politiques sociales; il faudrait donc une démarche critique pour faire le calcul de ces taux. Cet article démontre enfin les retombées bénéfiques de la collaboration interdisciplinaire et de l'exploitation des nouvelles techniques théoriques et empiriques en vue d'une meilleure compréhension des phénomènes sociaux. The published Canadian homicide rate indicates a steady downward trend since the mid-1970s. Yet the conventional homicide rate inaccurately reflects the nature of homicide, when used as a social barometer, and should be supplemented with a new homicide rate, calculated using available demographic information about offender characteristics. This paper uses recent advances in statistical techniques to show that an age-adjusted homicide rate exhibits a significantly different trend than that of the conventional rate: (1) there was no structural break in the trend until the late 1980s; (2) until the early 1990s the trend was an increasing homicide rate; and (3) although the homicide rate has been decreasing since the mid-1990s, there is insufficient statistical evidence to suggest a new downward trend. These findings suggest that demographics and time series analysis are required to properly assess homicide trends, helping to isolate social variables so their effects on homicide rates can be more accurately determined. Homicide rates have a substantial affect on social policy and public opinion and therefore should be critically calculated. Finally, this paper demonstrates the benefits of cooperation between academic disciplines and the utility of taking advantage of the latest theoretical and empirical techniques to reach a better understanding of social phenomena.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".