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An Overview of Attitudes Toward Genetically Engineered Food

2018· review· en· W2804870457 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAnnual Review of Nutrition · 2018
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaNational Science Foundation
KeywordsNaturalnessGenetically engineeredGenetically modified organismGenetically modified foodOpposition (politics)Environmental ethicsBiotechnologyBiologyPolitical sciencePoliticsGeneticsLaw

Abstract

fetched live from OpenAlex

Genetically engineered food has had its DNA, RNA, or proteins manipulated by intentional human intervention. We provide an overview of the importance and regulation of genetically engineered food and lay attitudes toward it. We first discuss the pronaturalness context in the United States and Europe that preceded the appearance of genetically engineered food. We then review the definition, prevalence, and regulation of this type of food. Genetically engineered food is widespread in some countries, but there is great controversy worldwide among individuals, governments, and other institutions about the advisability of growing and consuming it. In general, life scientists have a much more positive view of genetically engineered food than laypeople. We examine the bases of lay opposition to genetically engineered food and the evidence for how attitudes change. Laypeople tend to see genetically engineered food as dangerous and offering few benefits. We suggest that much of the lay opposition is morally based. One possibility is that, in some contexts, people view nature and naturalness as sacred and genetically engineered food as a violation of naturalness. We also suggest that for many people these perceptions of naturalness and attitudes toward genetically engineered food follow the sympathetic magical law of contagion, in which even minimal contact between a natural food and an unnatural entity, either a scientist or a piece of foreign DNA, pollutes or contaminates the natural entity and renders it unacceptable or even immoral to consume.

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.

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 score0.854
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.151
GPT teacher head0.387
Teacher spread0.236 · 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