Risks of Arrest across Drug Markets: A Capture-Recapture Analysis of “Hidden” Dealer and User Populations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Capture-recapture methodologies have been used to estimate the size of the hidden population of active offenders on the basis of the observed properties of the truncated distribution of arrested offenders. We use this approach to estimate the odds of arrest of marijuana, cocaine, crack, and heroin dealers and users in one Canadian province (Quebec). Findings indicate that risks of being arrested are much higher for sellers than for consumers and that this gap widens for the more harmful drugs. Findings also show, however, that vulnerability to arrest was significantly higher for marijuana users than for others users and that dealers in the smaller but more harmful drug markets (crack and heroin) manage to experience lower aggregate risks of arrest than cocaine or marijuana dealers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it