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
OBJECTIVE: To review: (1) the extent and frequency of street racing and its consequences; (2) the characteristics of street racers; (3) explanatory theories for street racing; (4) the legal issues; and (5) the best methods of preventing street racing. METHODS: Review of academic and other literature. RESULTS: Very limited official statistics are available on street racing offenses and related collisions, in part because of the different jurisdictional operational definitions of street racing and the ability of police to determine whether street racing was a contributing factor. Some data on prevalence of street racing have been captured through social surveys and they found that between 18.8 and 69.0 percent of young male drivers from various international jurisdictions have reported street racing. Moreover, street racing is found to be associated with other risky behaviors, substance abuse, and delinquent activities. The limited evidence available on street racing suggests that it has increased in the last decade. CONCLUSIONS: Street racing is a neglected research area and the time has come to examine the prevalence and causes of street racing and the effectiveness of various street racing countermeasures.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it