The Speeding Attitude Scale and the Role of Sensation Seeking in Profiling Young Drivers at Risk
Bibliographic record
Abstract
Seven driving attitude scales representing driving behaviors and beliefs about driving were created and initially validated using 257 undergraduate students (168 females, 89 males) in Study 1. However, the Speeding Attitude Scale (SAS) accounted for most of the strength of the intercorrelations among these scales and discriminant classification analyses showed that SAS dominated the other scales as a sufficient explanation for having speeding tickets. Study 2, using 180 students (75 males, 105 females), replicated findings regarding the significant but low correlation between SAS and speeding tickets, and was significantly correlated with Zuckerman's Sensation Seeking Scale (SSS). Replication also showed that males had higher SAS scores and more speeding tickets. Accidents were typically a function of sex, increasing age, and variables related to recent accident history. Objective sources of speeding attitude confirmation may enhance the future validity of the SAS. Potential interventions for being a safe passenger and attitudinal control in the training of young drivers were discussed.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".