Bibliographic record
Abstract
People in larger bodies often experience weight bias within physical activity settings, which significantly effects their relationship with physical activity (1). Weight bias refers to negative weight related judgements that are often made towards people living in larger bodies. People in larger bodies are often encouraged to exercise for weight management but are also consistently mistreated and judged in public physical activity spaces (2). This creates a lose-lose situation fostering fear of judgement, lower self-confidence and a greater tendency to avoid all forms of physical activity (3,4). Despite the known health benefits of physical activity, persistent bias undermines physical activity engagement and contributes to poor health outcomes. For fitness and health professionals, recognizing this dynamic is crucial. To foster truly inclusive physical activity environments, a weight inclusive approach is essential (5). This means prioritize movement for its diverse benefits like strength, stress relief, mobility, and mental well-being, rather than solely focusing on weight management (6). Practitioners must also reflect on their own biases, in order to create physical activity spaces that are accessible, safe, and respectful for all body sizes, emphasizing health and quality of life over, weight or size related outcomes (7).
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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 | 0.022 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".