Estimating Risks of Arrest and Criminal Populations: Regression Adjustments to Capture–Recapture Models
Bibliographic record
Abstract
The size of criminal populations is unknown, and policy decisions are typically based only on the number of offenses and offenders that come to the attention of the criminal justice system. However, the size of criminal populations may follow different trends than what is observed in official data. We use a regression-adjusted capture–recapture model to estimate the number of people at risk of arrest for offenses involving amphetamine-type stimulants (ATS) from arrests and rearrests occurring in Quebec, Canada, controlling for year of first arrest, age, and gender. The 4,989 individuals arrested were the visible part of an estimated 42,541 [36,936, 48,145] individuals otherwise at risk of arrest (12%). Additional results show that trends in criminal populations and risks of arrest vary across offense type and drug classifications.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | medium |
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 | 0.022 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 3 models reading the full record.
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".