Youthful Discretion: Police Selection Bias in Access to Pre-Charge Diversion Programs in Canada
Bibliographic record
Abstract
The increase use of formal youth diversion programs in Canada coincided with the enactment of the Youth Criminal Justice Act in 2003. Following the tenets of the labeling theory, the statute sought a balance that would help limit formal court intervention to increase fairness and accountability for youth committing minor offenses. Despite the perceived benefits, diversion programs have not escaped criticism. Some researchers contend pre-charge diversion programs that are based on police discretion may suffer from selection bias. Using police data from a local police service ( N = 6,479 cases) in Ontario, Canada, this article conducts a bivariate analysis to explore the personal characteristics of first-time offending youth (gender, race, and area of residence) and attempts to determine whether there are any differences in the youth being charged or diverted for minor drug possession and minor thefts. Results demonstrate variances in charging practices based on race. Race has a small but statistically significant impact on arrest decisions. In general, Black youth are more likely to be charged and less likely to be cautioned than White youth and youth from other racial backgrounds. The implications of these findings are discussed.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".