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Record W2172240429 · doi:10.1017/s0029665100000872

Ethical dilemmas in choosing a healthful diet: vote with your fork!

2000· review· en· W2172240429 on OpenAlexfundno aff
Marion Nestle

Bibliographic record

VenueProceedings of The Nutrition Society · 2000
Typereview
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsnot available
FundersNational Agricultural Statistics ServiceUniversity of CambridgeEnvironmental Defense FundYork UniversityU.S. Department of Agriculture
KeywordsBusinessAffect (linguistics)Government (linguistics)MarketingFood choicePromotion (chess)Consumption (sociology)Food processingPoliticsFood sciencePolitical scienceMedicinePsychology

Abstract

fetched live from OpenAlex

Dietary guidelines for health promotion and disease prevention in the USA recommend a consumption pattern based largely on grains, fruit and vegetables, with smaller amounts of meat and dairy foods, and even smaller amounts of foods high in fat and sugar. Such diets are demonstrably health promoting, but following them raises ethical issues related to the role of nutritionists in advising the public about healthful dietary choices, as well as to the role of the food industry in food production and marketing. In the USA a shift towards a more plant-based diet would affect the economic interests of producers of food commodities, food products and meals prepared outside the home; it would also affect the environment, food prices, trade with other countries (developing as well as industrialized) and relationships among the food industry, government agencies (domestic and international) and food and nutrition professionals. In a free-market economy any dietary choice has consequences for food producers. Thus, considerations of ethical dilemmas in choosing healthful diets suggest that food choices are political acts that offer opportunities for all parties concerned to examine the consequences of such choices and 'vote with forks'.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.878
Threshold uncertainty score0.887

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.290
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations15
Published2000
Admission routes1
Has abstractyes

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