Fish for Dinner? Balancing Risks, Benefits, and Values in Formulating Food Consumption Advice
Bibliographic record
Abstract
Many and complex factors underlie seemingly simple decisions about what to eat. This is particularly so for foods such as fish, which present consumers with both risks and benefits. Advice about what type of and how much fish to consume is abundant, but that advice is often confusing or contradictory, reflecting the differing mandates and orientations of those advising. We survey a range of issues that can and should be incorporated into dietary advice, and offer tools for health agencies tasked with providing it. We argue that risks and benefits should not be limited to direct physical health. Rather, socioeconomic and community factors, unintended or indirect effects, and nonhuman-health outcomes such as animal welfare and planetary health should also be considered and weighed. We provide examples of existing fish consumption guidance to highlight the conflicting messages that emerge when different sources of advice with singular aims of avoiding risk, gaining nutritional benefit, or sustaining fish populations are juxtaposed. We then offer tools borrowed from health and other fields to guide health agencies toward developing more comprehensive advice and targeting that advice for specific populations.
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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 | 0.031 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".