INCREASING MOTORIST COMPLIANCE AND CAUTION AT STOP SIGNS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study evaluated strategies to improve motorist compliance and caution at three stop-sign-controlled intersections with a history of motor vehicle crashes. The primary intervention was a light-emitting diode (LED) sign that featured animated eyes scanning left and right to prompt drivers to look left and right for approaching traffic. Data were scored from videotape on the percentage of drivers coming to a complete stop and the percentage of drivers looking right before entering the intersection. Observational data were collected on the percentage of right-angle conflicts (defined as braking suddenly or swerving from the path to avoid an intersection crash). The introduction of the LED sign according to a multiple baseline across the three intersections was associated with an increase in the percentage of vehicles coming to a complete stop at all three intersections and a small increase in the percentage of drivers looking right before entering the intersections. Conflicts between vehicles on the major and minor road were also reduced following the introduction of the animated eyes prompt.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it