The Value of Unhealthy Eating and the Ethics of Healthy Eating Policies
Bibliographic record
Abstract
Unhealthy eating can have value for individuals and groups, even while it has disvalue in virtue of being unhealthy. In this paper, we discuss some ways in which unhealthy eating has value and draw out implications for the ethics of policies limiting access to unhealthy food. Discussing the value and disvalue of unhealthy eating helps identify opportunities for reducing unhealthy eating that has little value, and helps identify opportunities for eliminating trade-offs between health and other values by making unhealthy food experiences healthier without eliminating their value. It also helps us think through when it is ethically acceptable, and when it might be ethically unacceptable, to limit valuable experience in order to promote health. Our discussion of the value and disvalue of eating is offered here as a necessary supplement to the familiar discussion of paternalism, autonomous choice, and public policy.
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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 | 0.039 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.081 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.009 | 0.009 |
| 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".