Fairness and Respect in Obesity Prevention Policies: A Response to David Buchanan
Bibliographic record
Abstract
In his response to our article (1,2), David Buchanan introduces some useful and important distinctions in the concepts of equality and autonomy. He highlights, for example, the distinction between inequality and inequity, which captures the insight that not all differences between people are unjust. Unjust inequalities are a subset of differences between people, and theories of justice can be defined by how they determine which of these differences are unjust. In addition, he points out that autonomy is not simply a matter of negative liberty, but also about a positive capacity to act. This understanding of autonomy is consistent with the account we offered in the paper, which underlines the importance of both the capacity to understand available options, and the capacity to act on the choices that one makes.
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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 | 0.044 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.073 |
| Scholarly communication | 0.016 | 0.030 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.057 | 0.078 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".