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Record W2017331548 · doi:10.1093/scipol/sct003

A distorted regulatory landscape: Genetically modified wheat and the influence of non-safety issues in Canada

2013· article· en· W2017331548 on OpenAlex
Jean‐Michel Marcoux, Lyne Létourneau

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueScience and Public Policy · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsRegulatory agencyAuthorizationAgency (philosophy)Flexibility (engineering)Public economicsPolitical scienceEnvironmental planningBusinessPublic administrationEconomicsSociologyGeographySocial scienceManagement

Abstract

fetched live from OpenAlex

Drawing on the institutional analysis and development framework, this paper explores the likely influence of socio-economic issues on the processing of the application for the authorization for genetically modified wheat by the Canadian Food Inspection Agency (CFIA) in the period 2002–4. As an attempt to explain why the CFIA regulators asked for additional environmental data relating to the unconfined release of this crop, and refrained from making a regulatory decision, this analysis focuses on the interaction between the rules that frame the formal approval process and the involvement of various actors in a lively social debate. It argues that the flexibility provided by the regulatory decision-making process, combined with the socio-economic issues that were forcefully raised by interest groups, academics and parliamentary committees, created a distorted regulatory landscape that led regulators to further scrutinize the environmental impacts of this seed.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.827
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.227
Teacher spread0.217 · 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