The Ergonomics of Dishonesty
Bibliographic record
Abstract
Research in environmental sciences has found that the ergonomic design of human-made environments influences thought, feeling, and action. In the research reported here, we examined the impact of physical environments on dishonest behavior. In four studies, we tested whether certain bodily configurations-or postures-incidentally imposed by the environment led to increases in dishonest behavior. The first three experiments showed that individuals who assumed expansive postures (either consciously or inadvertently) were more likely to steal money, cheat on a test, and commit traffic violations in a driving simulation. Results suggested that participants' self-reported sense of power mediated the link between postural expansiveness and dishonesty. Study 4 revealed that automobiles with more expansive driver's seats were more likely to be illegally parked on New York City streets. Taken together, the results suggest that, first, environments that expand the body can inadvertently lead people to feel more powerful, and second, these feelings of power can cause dishonest behavior.
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".