Coolness: An Empirical Investigation
Bibliographic record
Abstract
Some people are routinely described as “cool,” but it is unknown whether this descriptor conveys trait-like information beyond mere likability or popularity. This is the first systematic quantitative investigation of coolness from a trait perspective. Three studies of North Americans (N = 918) converged to identify personality markers for coolness. Study 1 participants described coolness largely by referring to socially desirable attributes (e.g., social, popular, talented). Study 2 provided further evidence of the relationship between coolness and social desirability, yet also identified systematic discrepancies between valuations of coolness and social desirability. Factor analyses (Studies 2 and 3) indicated that coolness was primarily conceptualized in terms of active, status-promoting, socially desirable characteristics (“Cachet coolness”), though a second orthogonal factor (“Contrarian coolness”) portrayed cool as rebellious, rough, and emotionally controlled. Study 3, which examined peer valuations of coolness, showed considerable overlap with abstract evaluations of the construct. We conclude that coolness is reducible to two conceptually coherent and distinct personality orientations: one outward focused and attuned to external valuations, the other more independent, rebellious, and countercultural. These results have implications for both basic and applied research and theory in personality and social psychology.
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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 | 0.011 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".