A desire for a loud car with a modified muffler is predicted by being a man and higher scores on psychopathy and sadism
Bibliographic record
Abstract
BACKGROUND: Although people are familiar with loud automobiles, equipped with modified mufflers to increase the volume, it is unclear who is most attracted to these vehicles. PARTICIPANTS AND PROCEDURE: A sample of 529 (52% men) undergraduate business students were surveyed and were asked if they viewed their car as an extension of themselves, how much they thought loud cars were "cool", and if they would make their car louder with muffler modifications. Cronbach's α of the three car items was .76; therefore an aggregate was generated. Also a self-report measure of the dark tetrad was completed. RESULTS: Using linear regression, the car aggregate was predicted by being a man and having higher scores on psychopathy and sadism, with the model accounting for approximately 29% of the variance. CONCLUSIONS: As car modifications are illegal in some countries, these findings may be of interest to those heading campaigns to halt these activities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".