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Record W1594293621 · doi:10.22004/ag.econ.51652

NONPARAMETRIC ANALYSIS OF ATTITUDES TOWARD RISKY CROPS: A PLANT MOLECULAR FARMING CASE STUDY

2009· preprint· en· W1594293621 on OpenAlexaboutno aff
Michele M. Veeman, Dmitriy Volinskiy, Wiktor Adamowicz

Bibliographic record

VenueAgEcon Search (University of Minnesota, USA) · 2009
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessAgricultureFood safetyNonparametric statisticsSurvey data collectionEnvironmental healthAgricultural scienceEconomicsEnvironmental scienceGeographyMedicineStatisticsEconometricsMathematics

Abstract

fetched live from OpenAlex

Research on plant molecular farming (PMF) is supported by public and private sectors in Canada. This may lead to benefits of new or cheaper medicines, industrial products and foods, but also be the source of appreciable risks to food safety from contamination by PMF materials, as well as potential environmental risks and costs. This study uses data from a 2005 nation-wide Canadian survey to gain insights into citizens’ perceptions of PMF benefits and risks. A series of nonparametric tests are conducted on ordinal survey data on risk and benefit assessments of respondents. PMF is not seen as a major threat to food safety or the environment, but as a moderate indirect risk. The use of PMF to produce better and cheaper medical drugs appears to have the best benefits-to-risks ratio, while using PMF to produce more nutritious and cheaper food has the least favorable ratio.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.068
GPT teacher head0.286
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2009
Admission routes1
Has abstractyes

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