Changing fit and fat bias using an implicit retraining task
Bibliographic record
Abstract
OBJECTIVE: To change implicit and explicit bias so that active obese people are regarded as more fit and normal weight sedentary people as less fit. DESIGN: Study one created a questionnaire that measured perceptions of active obese persons and sedentary normal weight persons on fitness-related items. Study two used a modified visual probe task to retrain perceptions regarding active obese persons and sedentary normal weight persons. MAIN OUTCOME MEASURES: Self-reported explicit bias was measured with a questionnaire and implicit bias was measured with response times collected during a visual probe task. RESULTS: The questionnaire reliably measured 'fitness and fatness' perceptions. In study two, pairing images of active obese persons with positive activity-related words resulted in active obese persons being explicitly rated more fit; pairing images of normal weight sedentary persons with negative words associated with sedentary lifestyles increased endorsement of normal weight people as unfit. There were no changes in implicit bias. CONCLUSIONS: Bias regarding how body weight is thought of relative to fitness can be altered by pairing images of obese persons being active with words such as 'health' and 'fit'. This is evidence that representations of persons of all body weight should be used when promoting physical activity.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".