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Record W4392346224 · doi:10.1080/23299460.2024.2304383

The need for more inclusive deliberation on ethics and governance in agricultural and food biotechnology

2024· article· en· W4392346224 on OpenAlexaff
Catherine Kendig, Theresa Selfa, Paul Β. Thompson, Raymond Anthony, Wenda K. Bauchspies, Gwendolyn Blue, Ashmita Das, R. M. Harrison, Christopher R. Henke, Shan Jin, Jennifer Kuzma, Forbes Lipschitz, K.R. Richter, Morgan Ruelle, Timothy R. Silberg, Bruno Takahashi

Bibliographic record

VenueJournal of Responsible Innovation · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsUniversity of Calgary
FundersNational Institute of Food and AgricultureU.S. Department of Agriculture
KeywordsDeliberationAgricultural biotechnologyCorporate governanceAgriculturePolitical scienceEngineering ethicsBiotechnologyEnvironmental ethicsBiologyEconomicsManagementEngineeringPoliticsEcology

Abstract

fetched live from OpenAlex

An inclusive and socially legitimate governance structure is absent to address concerns over new agricultural biotechnologies. Establishing an agricultural bioethics commission devoted to inclusive deliberation on ethics and governance in agricultural and food biotechnology is urgent. Highlighting the social and ethical dimensions of current agricultural bioengineering disputes in the food system, we discuss how a nationally recognized policy forum could improve decision-making and increase public understanding of the issues. We clarify ways the concepts that are used to categorize food and frame governance of food affect consumer choices, and how dissemination of information and the mode of dissemination can contribute to social inequities. We cite the record of medically-oriented bioethic commissions and the history of international bioethic commissions in support of our argument, and end by discussing what such a commission dedicated to agriculture and food issues could reasonably be expected to achieve.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score0.204

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.034
GPT teacher head0.308
Teacher spread0.273 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations4
Published2024
Admission routes1
Has abstractyes

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