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Record W2038656131 · doi:10.2202/1535-1661.1205

Agricultural Biotechnology: Legal Liability Regimes from Comparative and International Perspectives

2006· article· en· W2038656131 on OpenAlexaffabout
Stuart J. Smyth, Drew L. Kershen

Bibliographic record

VenueGlobal Jurist Advances · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsLiabilityAgricultural biotechnologyStatutory lawPolitical scienceNegotiationLawAgricultureBiology

Abstract

fetched live from OpenAlex

As agricultural biotechnology has become an agronomic alternative, discussion has emerged about what legal liabilities, if any, exists for those who create, distribute, and produce transgenic seeds and crops. Many governments have debated legal liability as related to agricultural biotechnology. In this article, the authors offer fresh insights on legal liability from comparative law and international law perspectives. The article begins by comparing Canadian and American legal liability regimes in agricultural biotechnology. Using this North American comparison as background, the article then discusses liability issues by contrasting the statutory regimes from Denmark and Germany. Once the comparisons and contrasts between Canadian, American, Danish, and German law have been presented, the article focuses on the on-going discussion of legal liability and agricultural biotechnology at the Meeting of the Parties (MOP) of the Cartagena Protocol on Biosafety (BSP). The authors posit that understanding the comparisons and contrasts between Canada, the United States, Denmark, and Germany assists greatly in understanding the issues and debates about legal liability and agricultural biotechnology at the international level in the BSP negotiations.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.750
Threshold uncertainty score0.243

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.013
GPT teacher head0.265
Teacher spread0.252 · 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.

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

Citations11
Published2006
Admission routes2
Has abstractyes

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