THE DRUG RECOGNITION EXPERT POLICE OFFICER: A RESPONSE TO DRUG-IMPAIRED DRIVING
Bibliographic record
Abstract
Law enforcement agencies in thirty-four United States and British Columbia, Canada rely on Drug Recognition Expert (DRE) officers to apprehend the drug-impaired driver. DREs utilize a systematic and standardized twelve-step procedure to reach three determinations: (1) that the driver is impaired; (2) that the driver is impaired by drugs, rather than suffering from a medical condition that requires intervention; and (3) the category of drug(s) that is causing the impairment. The admissibility of DRE opinion testimony, usually in conjunction with corroborative toxicological analysis, has been upheld in many courts throughout the U.S. The seven DRE drug categories are not based on shared chemical structures, legality, or use if any in treatment of illness or disease. Rather, the categories are based on shared patterns of signs and symptoms. The categories used by DREs are Central Nervous System (CNS) Depressants, Inhalants, Phencyclidine, Cannabis, CNS Stimulants, Hallucinogens, and Narcotic Analgesics. The prevalence of poly-drug use complicates the DRE determinations. This paper provides an overview of the DRE procedures, DRE drug categories, and the DRE training program. For the covering abstract see ITRD E106992.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".