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Record W2088884029 · doi:10.1177/0270467607300638

Tension on the Farm Fields: The Death of Traditional Agriculture?

2007· article· en· W2088884029 on OpenAlexaffabout
Chidi Oguamanam

Bibliographic record

VenueBulletin of Science Technology & Society · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsUniversity of OttawaDalhousie University
Fundersnot available
KeywordsAgricultureAgricultural biotechnologyPluralism (philosophy)Intellectual propertyPhenomenonJurisprudencePolitical scienceLaw and economicsBiotechnologyEnvironmental ethicsSociologyLawEpistemologyBiologyEcologyPhilosophy

Abstract

fetched live from OpenAlex

Taking into account the historic transitions and progressions in agricultural science, this article examines the emergence of the phenomenon of agricultural biotechnology. It identifies pivotal sites of tension between agricultural biotechnology and alternative approaches to agriculture. The article identifies two distinct sources of contemporary social tension around agricultural science. First, it identifies the epistemological fault line and examines how the latter is promoted by intellectual property. Second, it spotlights the gene-wandering syndrome—a byproduct of genetic modification—and evaluates its impact on the escalating tension in our agricultural communities. Drawing from recent court decisions in Canada, the article recognizes the present urgency for a better jurisprudence and practical regulatory policy on aspects of agricultural biotechnology to mediate current tensions in those communities. It argues that judicial and policy response must be predicated on recognition of agro-epistemic pluralism and an understanding of broader socioeconomic impact of agro-biotechnology on alternative forms of agriculture.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.046
Scholarly communication0.0080.009
Open science0.0010.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.037
GPT teacher head0.239
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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations3
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueBulletin of Science Technology & SocietySame topicGenetically Modified Organisms ResearchFrench-language works237,207