1 Using Police Crime Surveys to Study Drug Abuse
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".