NO PAIN NO GAIN? EXAMINING THE GENERALIZABILITY OF THE EXERCISER STEREOTYPE TO MODERATELY ACTIVE AND EXCESSIVELY ACTIVE TARGETS
Bibliographic record
Abstract
This study examined whether the exerciser stereotype extends to moderately and excessively active individuals, and if the rater's self-described exercise status moderates the effect. Using a 2 (exercise status) x 5 (target type) design, 456 participants read a description of a typical exerciser, a nonexerciser, an active-living target, an excessive exerciser, or a control target and rated her on 12 personality and 8 physical dimensions. MANOVAs revealed a significant main effect for target type on both personality and physical dimensions (ps < .05). For most of the personality attributes, the exerciser and active-living target were rated more favorably than were the excessive exerciser, nonexerciser and control. For the physical attributes, the typical exerciser, active-living target and excessive exerciser were rated more highly than were the nonexerciser and control. The exercise status x target type interaction was significant for only three physical dimensions. Overall, results indicate that the exerciser stereotype exists regardless of the rater's exercise status and can generalize to different levels of physical activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".