Do Nonexercisers Also Share the Positive Exerciser Stereotype?: An Elicitation and Comparison of Beliefs about Exercisers
Bibliographic record
Abstract
The purpose of this research was to examine whether exercisers and nonexercisers are rated similarly on a variety of characteristics by a sample of randomly selected regular exercisers, nonexercisers who intend to exercise, and nonexercisers with no intention to exercise. Previous research by Martin Ginis et al. (2003) has demonstrated an exerciser stereotype that advantages exercisers. It is unknown, however, the extent to which an exerciser stereotype is shared by nonexercisers, particularly nonintenders. Following an item-generation procedure, a sample of 470 (n=218 men; n=252 women) people selected using random digit dialing responded to a questionnaire assessing the extent to which they agreed that exercisers and nonexercisers possessed 24 characteristics, such as "happy," "fit," "fat," and "lazy." The results strongly support a positive exerciser bias, with exercisers rated more favorably on 22 of the 24 items. The degree of bias was equivalent in all groups of respondents. Examination of the demographic characteristics revealed no differences among the three groups on age, work status, or child-care responsibilities, suggesting that there is a pervasive positive exerciser bias.
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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 | 0.001 |
| 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".