A New Look at NHTSA's Evaluation of the 1984 Charlottesville Sobriety Checkpoint Program: Implications for Current Checkpoint Issues
Bibliographic record
Abstract
OBJECTIVE: Currently, the implementation of sobriety checkpoint programs, which have been demonstrated to be effective in reducing alcohol-related crashes, is limited by the belief that they require large consignments of police officers and result in few arrests. However, one of the earliest evaluations of a checkpoint program in Charlottesville, Virginia, demonstrated that effective checkpoints could be mounted in which police officers made as many arrests as officers on regular patrols. That study was printed by the NHTSA but was not published in a peer-reviewed journal. Because of its significance to current issues in the staffing of and procedures for checkpoint operations, this article reanalyzes the results of that study and describes the procedures implemented in checkpoints. METHODS: A before-and-after control design was used to measure the change in nighttime crashes from three baseline years to the program year. Two analyses were conducted: the first on the percentage of all crashes occurring at night in the test city--Charlottesville--and the second on the percentage of all nighttime crashes in the state of Virginia that occurred in the test city. In addition, three waves of random-digit-dialing telephone surveys were conducted: one before and two during the checkpoint program in the test city, and the comparison city, Blacksburg. Finally, the number of impaired-driving arrests per officer hour at the checkpoints was compared with the number of arrests per hour by officers on regular patrol and the effect on arrests of the use of passive sensors was determined. RESULTS: The monthly percentage of nighttime crashes in Charlottesville was reduced by 17% (p = 000) in relation to the baseline level. The percentage of nighttime crashes in the state of Virginia that occurred in Charlottesville was reduced by 11% (p = .013) from baseline levels. Drivers arrested at checkpoints had lower BACs than those arrested by the regular patrols; however, the conviction rates were the same. The arrest per officer hour did not differ significantly between the two types of enforcement operations. Awareness of the checkpoint activity was high (72%) among nighttime at-risk drivers in the test city. Half reported seeing a checkpoint operation, and a quarter reported being interviewed. Use of a passive alcohol sensor by officers at the checkpoint increased arrests by almost a factor of three. CONCLUSIONS: The results of the evaluation suggest that small-scale sobriety checkpoints can be implemented as part of the regular enforcement program in moderate-sized jurisdictions and that they can be as efficient in producing arrests as standard enforcement patrols, particularly if passive alcohol sensors are used.
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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 | 0.034 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".