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Record W3173191299 · doi:10.21423/jrs-v09i2ludlow

Socio-Economic Considerations and Potential Implications for Gene-Edited Crops

2021· article· en· W3173191299 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.

Bibliographic record

VenueJournal of Regulatory Science · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCommercializationBiosafetyCLARITYConsistency (knowledge bases)Variety (cybernetics)BiotechnologyBusinessRisk analysis (engineering)Genetically modified organismEnvironmental planningPolitical scienceComputer scienceBiologyMarketingGeography

Abstract

fetched live from OpenAlex

Regulatory clarity and efficiency are increasingly important for the successful commercialization of technologies resulting from public and private R&D investments. This article examines recent developments in the movement away from mostly science-based risk assessment regulatory and variety approval systems focusing on human and animal health and environmental safety to hybrid systems that include assessment of socio-economic considerations allowed for under the auspices of the Cartagena Protocol on Biosafety. We propose that socio-economic consideration assessments can be grouped into three methodological categories: empirically based, legally grounded and consensus approaches. Our exploration of developments in the three categories reveals gaps in data, consistency and methodology rigor, that must be addressed for efficient and reliable socio-economic consideration assessment. We assess the potential impacts of these gaps on the regulation of gene-edited crop varieties, concluding that if gene-edited crops are regulated as genetically modified crops, they will endure the same fate, that is, lengthy regulatory approval processes and failure to be commercialized. The end result being that the regulatory burdens in potential adopting and food insecure countries will prevent important new crop varieties from reaching farmers and producers for their use. https://doi.org/10.21423/jrs-v09i2ludlow

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.892
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.031
GPT teacher head0.275
Teacher spread0.244 · 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