THE DRUG RECOGNITION EXPERT OFFICER:SIGNS OF DRUG IMPAIRMENT AT ROADSIDE
Bibliographic record
Abstract
Approximately 5,000 Drug Recognition Expert (DRE) officers serve in the United States and British Columbia, Canada. Law enforcement agencies in Australia, England and Germany have also adopted variations of DRE procedures. The primary function of a DRE is to evaluate drivers for impairment due to drugs other than alcohol. If the DRE evaluation is positive for drugs, the DRE will identify the category (s) of drug (s), based on shared patterns of effects, causing the impairment. The DRE evaluation typically occurs following an impaired driving arrest by a non-DRE officer who suspects drug impairment. Drug impairment is typically suspected when the impairment is not consistent with the driver's alcohol level as determined by a chemical test. The involvement of a DRE officer depends upon the ability of an arresting officer to quickly identify cues that suggest drug impairment. Generally, the driving under the influence investigation involves three phases: (1) Illegal or erratic driving actions that alert the officer to the possibility of alcohol and/or drug impairment; (2) Face to face encounter with the driver during which the officer may discover indicia, including physiological signs, of drug impairment; and (3) Administration of the Standardized Field Sobriety Test (SFST) battery, concluding with the decision to arrest or release the driver. The decision to administer the SFST battery is largely based upon evidence of impairment recognized during the phase two encounter. This paper summarizes Phase 2 cues that are taught in SFST and DRE courses, and compares these cues with those documented in a sample of actual DUI-DRE investigations. It will discuss the results and implications of this comparison for the DRE curricula, as well as for the training of the non-DRE traffic enforcement officer. In addition, this paper will provide an overview and update of the DRE program, procedures, training, and court decisions. (A) For the covering abstract of the conference, see ITRD Abstract No. E201067.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".