Weight bias and identity characteristics among students at a public university in Southern Brazil
Bibliographic record
Abstract
ABSTRACT Objective Despite the consequences of weight discrimination for health inequities, its relationship with identity characteristics remains poorly understood. We investigated whether and to what extent discrimination attributed to body weight is linked to sociodemographic and identity factors. Methods This cross-sectional study is based on a representative sample of undergraduate students from the Federal University of Santa Catarina. Information on perceived discrimination was collected using the brief version of the Explicit Discrimination Scale. Socioeconomic and demographic data were also collected. Results: The results showed that 22.8% of the sample reported experiencing discrimination for being “fat or thin” throughout their lives. Perceived weight discrimination was higher among respondents whose household heads had completed up to high school education, and among those who were overweight and rated their health as “poor.” Conclusion Perceived weight discrimination was associated with significant factors linked to the stigmatization and pathologization of body weight. These findings should be considered in more inclusive approaches aimed at counteracting the embodiment of social inequalities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".