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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".