1 Using Police Crime Surveys to Study Drug Abuse
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
In most countries of the world, information reported to the national statistical agency by police agencies, based on their operational files, constitutes the principal survey of criminal activity (Newman 1999: 10-11; Tremblay 1999), including criminal drug abuse. This is because the police are the official agency that is closest to the actual commission of crime. Other official agencies, such the criminal courts or correctional system, also compile caseload statistics, but they are farther removed from crime, so the volume and characteristics of cases and criminals which they record are progressively biased by selective attrition, due to pre-court screening, prosecutorial discretion, and other contingencies of the court process. In Canada, all police agencies report crime data to the Canadian Centre for Justice Statistics, a branch of Statistics Canada, in the format of the Uniform Crime Reporting Survey (“UCR”), which is similar to the UCR established in the USA in the early twentieth century. Police crime surveys such as the UCR have certain advantages and disadvantages as sources of information on particular crimes, such as drug-related crime. Of course, police crime surveys only include drug abuse that has been criminalized—that is, made a criminal offence. Abuse of legally obtained drugs is therefore excluded. Advantages of police surveys include
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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