Fairness and respect in obesity prevention policies: a response to David Buchanan
Bibliographic record
Abstract
Fairness and respect in obesity prevention policies: a response to David Buchanan I n 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.While we agree with his distinctions, we would like to raise some questions about the conclusions that he offers.In particular, he puts forward two claims: First, he claims that taxing soda and Sugar Sweetened Beverages (SSB) and banning the sale of larger SSB containers are regressive policies, and therefore unjust.Second, Buchanan claims that telling poor people what they can or cannot buy with food stamps is insulting.We will address each claim in turn.Taxing soda and banning the sale of larger soda pop containers is regressive and unjust.Regressive measures are those that take a higher percentage of the income of low-income people.There are countless regressive taxes and only some are considered unfair-for example, a sales tax on groceries is commonly considered unfair because it is excessively burdensome on low-income people.Food is a necessary expense, and a tax on groceries would make these necessary goods more expensive to those already struggling to afford them.For this reason, a regressive tax on groceries is seen as unfair.The same reasoning does not apply to soda.First, soda is not a necessary good.While many people enjoy drinking soda, soda does not alleviate hunger, and it has no nutritional value over and above the calories it contains (3).Furthermore, SSB can be harmful to individuals.Along with other unhealthy foods, sugary drinks contribute to high rates of obesity, diabetes, heart disease, and other chronic diseases.Thus, these policies would not restrict access to any
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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 | 0.042 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.076 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.056 | 0.076 |
| 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".