Exercising impressive impressions: the exercise stereotype in male targets
Bibliographic record
Abstract
The main purpose of this study was to investigate the exercise stereotype and the non-exerciser stereotype on male targets, and the moderating effects of impression motivation in 184 female and 73 male Swedish undergraduate students. The participants read a description of one of the following male targets: a typical exerciser, an active living target, an excessive exerciser, a non-exerciser, or a control target, and then rated these targets on 12 personality (e.g. lazy-hard worker, dependent-independent) and eight physical (e.g. scrawny-muscular, sick-healthy) dimensions. They also completed the Impression Motivation scale of the Self-Presentation in Exercise Questionnaire, measuring motivation to self-present as an exerciser. MANCOVAs demonstrated a significant main effect for both personality and physical attributes (P<0.05). Overall, the typical exerciser, the active living target, and the excessive targets received more positive ratings than, in particular the non-exerciser target but also the control target. The non-exerciser target was rated less favorably compared with the control target. The impression motivation of the participants moderated the exercise status/rating relationship for the physical but not the personality attributes. The results of the study are discussed in the context of gender and cultural aspects of the exercise stereotype phenomenon.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".