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Record W317793933

Assessing the Science of Genetically Modified Crops: The New Frontier of Public Health Policy

2007· article· en· W317793933 on OpenAlexvenueno aff
Jacob Shelley

Bibliographic record

VenueHealth law review · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsnot available
Fundersnot available
KeywordsPublic healthHealth policyPolitical scienceAdjudicationPublic health lawPoliticsPublic policyPrecautionary principlePublic relationsInternational healthLaw and economicsPublic administrationLawHealth careSociologyMedicine
DOInot available

Abstract

fetched live from OpenAlex

Good should inform public health policy. (1) Such a claim seems self-evident; however, when matters of public health policy are addressed by Courts, role of becomes murky. At best of times, when is unambiguous, public health decisions necessitate that a complex matrix of legal, political, economic and ethical considerations be weighed against one another. (2) More often public health decisions are based on uncertainties, as the information that most scientific research provides to health and environmental regulation is incomplete and inconclusive, both in identifying and in quantifying risks that these hazards pose. (3) It is task of public health agencies to formulate responses to these risks, identifying and evaluating relevant prior to making a decision. (4) Should a matter of public health come before courts task of deciphering no longer belongs to public health agencies, but is vested in judiciary. If is ambiguous--as is often case (5)--does judiciary possess requisite information or expertise to adjudicate such matters? Are courts appropriate venue for evaluating public health decisions? These questions are further complicated by novel and controversial sciences, which are not only contentious in their own right but often represent a battle between publicly-funded health agencies and multinational corporations. The following aims to elucidate challenges of adjudicating matters of public health where is ambiguous by distilling lessons learned from what arguably represents most polemical public health issue courts have faced: tobacco. This discussion will be facilitated by using case study of what stands to be one of new frontiers of public health policy in courts, genetically modified (GM) crops. GM crops present a unique problem for public health decision-making. A new area of research, debate concerning GM crops is fiercely polemical. On one side of debate are those warning of risks, arguing that such risks should be avoided until further research can be undertaken. On other side are those contending that benefits associated with GM crops far outweigh risks. (6) A vast amount of information has been disseminated to general public and scientific community espousing various viewpoints. (7) All rely to some degree on scientific proofs. Given ambiguities surrounding of GM crops and their undetermined impact on human health, should a public health concern be brought before courts, task of weighing evidence would be formidable. (8) Several lessons from three decades of tobacco litigation, however, can help ensure that quality of relied upon is not swayed by partisan interests. Tobacco litigation, in many respects, serves as archetype for achieving public health goals. Three decades of tobacco litigation have assisted in articulation and development of public health policies; they have also resulted in some substantial success stories, with victims being compensated by industry. On downside, tobacco industry was provided with sufficient time to develop a counter-strategy; namely, science movement. The term science was utilized by tobacco industry during litigation to deconstruct and dismantle scientific evidence presented by plaintiffs' expert testimony regarding on smoking and health. (9) It was an attempt to ridicule any research that threatened their interests, paying no regard to quality of research. (10) In addition, it allowed to be bent to achieve pre-determined goals. (11) Industry documents reveal that tobacco industry intentionally undermined scientific studies, falsified data, manufactured uncertainty and propagated its own junk as a way to avoid liability. (12) As one tobacco industry executive acknowledged, Doubt is our product since it is best means of competing with 'body of fact' that exists in minds of general public. …

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)0.012
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.874
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
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 head0.173
GPT teacher head0.421
Teacher spread0.248 · 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 designOther design
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
Published2007
Admission routes1
Has abstractyes

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