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Genetically Modified Organisms as Public Goods: Plant Biotechnology Transfer in Colombia

2009· article· en· W1990633364 on OpenAlexaff
Christina Holmes, Janice Graham

Bibliographic record

VenueCulture & Agriculture · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAgricultural biotechnologyBiotechnologyIntellectual propertyNegotiationPublic goodFood securityGenetically modified organismAgricultureGenetic resourcesGenetic engineeringCall for bidsProductivityBusinessPolitical scienceBiologyEconomic growthEconomicsProcurementMarketingEcology

Abstract

fetched live from OpenAlex

Abstract This paper presents an exploration of biotechnology transfer and genetically modified organisms (GMOs) as “public goods” for Colombia. Plant biotechnology tenders the promise of providing “public goods” in the form of increased agricultural productivity, economic development, and food security. However, these each have the potential to benefit different groups of people. Colombian scientists recognize this when discussing the uses of genetic modification. We examine the goals for which Colombian scientists suggest plant genetic engineering has promise as well as the barriers they encounter using the technology. Research using genetic engineering is difficult due to a lack of resources, the need to negotiate intellectual property rights, and regulatory hurdles. Nevertheless, Colombian scientists suggested that genetic modification by Colombians is important, as transnational companies would not necessarily develop crops to meet Colombian needs. We argue that interpretive complexity is necessary to understand the desire of Colombian scientists to engage with biotechnology.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.021
GPT teacher head0.223
Teacher spread0.202 · 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.

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

Citations5
Published2009
Admission routes1
Has abstractyes

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