Guidelines for Developing a High-Visibility Enforcement Campaign to Reduce Unsafe Driving Behaviors among Drivers of Passenger and Commercial Motor Vehicles: A Selective Traffic Enforcement Program (STEP) Based on the Ticketing Aggressive Cars and Trucks (TACT) Pilot Project
Bibliographic record
Abstract
The goal of Selective Traffic Enforcement Programs (STEPs) is to induce motorists to drive safely. To achieve this goal, the STEP model combines intensive enforcement of a specific traffic safety law with extensive communication, education, and outreach informing the public about the enforcement activity. First used in Canada, the evolution of STEPs has brought us the high-visibility enforcement campaigns popularized by the National Highway Traffic Safety Administration’s (NHTSA’s) Click It or Ticket seat belt program. Therefore, throughout this guide, the terms high-visibility enforcement campaign and STEP are used interchangeably. In 2004, Congress directed NHTSA and the Federal Motor Carrier Safety Administration (FMCSA) to work together to educate drivers of passenger vehicles on how to share the road safely with commercial motor vehicles. In response to this directive, these agencies worked with the Washington Traffic Safety Commission (WTSC) to develop and fully evaluate a demonstration project based on the STEP model. This guide is intended for State highway safety, law enforcement, and other professionals who work in the field of commercial vehicle safety. It provides guidelines for implementing a STEP to reduce unsafe driving behaviors among drivers of commercial and passenger motor vehicles. It draws on examples and lessons learned from the successful high-visibility enforcement campaign known as TACT (Ticketing Aggressive Cars and Trucks), which was developed in Washington State.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.014 |
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".