Bibliographic record
Abstract
The most important objective of the Department of Transport (DOT) in the Netherlands is to make Dutch freeways safer and less congested. To achieve these objectives, standard practice has been to influence the behavior of road users through punitive measures. To investigate the feasibility of doing the opposite, namely, influencing behavior by offering rewards, and of its usefulness if it worked, the DOT launched the Belonitor trial. Each year, tailgating and speeding cause much irritation on roadways. Moreover, these violations often play a role in accidents and congestion. The Belonitor trial therefore focuses on two preferred modes of behavior: maintaining sufficient distance and maintaining the applicable maximum speed. The lease company LeasePlan Nederland N.V. (LPNL) fitted 62 lease cars with equipment that recorded whether drivers maintained sufficient distance from the car ahead and drove within the posted speed limit. The equipment included a display that continuously showed drivers their following distance and speed. LPNL rewarded lease-car drivers for good driving behavior over a 16-week period. The data obtained from surveys, interviews, and the in-car system indicate that feedback and rewards have a strong positive effect on safe driving behavior. The trial results also indicated differences in how drivers handle speed and following distance. In the Belonitor trial, DOT traffic safety objectives were successfully combined with profit goals of the lease company.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".