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
Motor racing includes high speed driving and risky maneuvers and can result in negative outcomes for both spectators and drivers. Interest in motorsports is also associated with risky driving attitudes and behaviors on public roads as well as with individual difference variables, such as sensation seeking. However, whether the links between motorsports involvement and risky driving tendencies differ for spectators and drivers has remained mainly unexamined. The aim of this study was to investigate the relationships between thrill seeking, attitudes toward speeding, and self-reported driving violations among a sample of motorsports spectators and drivers.A web-based survey was conducted and sampled 408 members and visitors of car club and racing websites in Ontario, Canada. The questionnaire included measures of (i) motorsports involvement, (ii) thrill seeking (Driver Thrill Seeking Scale), (iii) attitudes (Attitudes toward Speed Limits on Roadways and Competitive Attitudes toward Driving Scale); (iv) self-reported driving violations (adapted from Driver Behaviour Questionnaire), and (v) background variables. Path analysis was performed to test the relationships among the variables.For both spectators and drivers, thrill seeking directly predicted driving violations; competitive attitudes toward driving further mediated this relationship. Attitudes toward speed limits, however, mediated the relationship between thrill seeking and violations only for drivers.We observed significant relationships among individual difference measures, motorsports involvement, speeding attitudes and violations that may inform road safety interventions, including differences in the relationships among thrill seeking, speeding attitudes, and violations for motorsports spectators and drivers.
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) | 0.002 | 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