Epistemological and ethical assessment of obesity bias in industrialized countries
Bibliographic record
Abstract
Bernard Lonergan's cognitive theory challenges us to raise questions about both the cognitive process through which obesity is perceived as a behaviour change issue and the objectivity of such a moral judgment. Lonergan's theory provides the theoretical tools to affirm that anti-fat discrimination, in the United States of America and in many industrialized countries, is the result of both a group bias that resists insights into the good of other groups and a general bias of anti-intellectualism that tends to set common sense against insights that require any thorough scientific analyses. While general bias diverts the public's attention away from the true aetiology of obesity, group bias sustains an anti-fat culture that subtly legitimates discriminatory practices and policies against obese people. Although anti-discrimination laws may seem to be a reasonable way of protecting obese and overweight individuals from discrimination, obesity bias can be best addressed by reframing the obesity debate from an environmental perspective from which tools and strategies to address both the social and individual determinants of obesity can be developed. Attention should not be concentrated on individuals' behaviour as it is related to lifestyle choices, without giving due consideration to the all-encompassing constraining factors which challenge the social and rational blindness of obesity bias.
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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 | 0.064 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.013 | 0.069 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.007 | 0.007 |
| 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".