Through a different lens: Examining the influence of culture and acculturation on perceptions of the female exerciser stereotype
Bibliographic record
Abstract
An exerciser stereotype in which exercisers receive more positive ratings on physical and personality attributes than those described as non-exercisers and control targets has been identified in previous research (e.g., Munroe-Chandler et al., 2012). Despite these positive ratings, the rate of participation in exercise for non-White Canadians is below that of White Canadians (Bryan et al., 2006). One factor that may influence exercise participation rates for ethnic minorities is acculturation to mainstream culture (Daniel et al., 2013). The purpose of the present study was to examine the female exerciser stereotype in light of both culture and individual acculturation. Participants (N = 510) read a vignette describing a female exerciser, and rated the target on personality and physical attributes before completing the Vancouver Index of Acculturation (Ryder et al., 2000). Results revealed no significant differences between White (n = 340) and non-White (n = 170) participants on ratings of personality and physical attributes (ps > .05). However, it was found that those who were more acculturated with mainstream Canadian culture rated the target higher on physical and personality attributes compared to those who were less acculturated to mainstream culture (ps < .05). Findings indicate that mainstream acculturation may be a more important factor when forming impressions of exercisers than an individual’s culture.Acknowledgments: Social Sciences and Humanities Research Council
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".