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Record W599351108

Bias and science in knowledge production: implications for the politics of drug regulation

2008· book-chapter· en· W599351108 on OpenAlexaboutno aff
John Abraham

Bibliographic record

VenueFigshare · 2008
Typebook-chapter
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsContext (archaeology)Political scienceConsumption (sociology)IrishRegulatory sciencePharmaceutical industryPolitical economyMedicineEconomicsSociologySocial sciencePharmacologyLawBiology
DOInot available

Abstract

fetched live from OpenAlex

Public concerns about the regulation of the pharmaceutical industry have intensified in recent years, not least because of a series of controversies about drugs such as those used in the treatment of depression, arthritis, and AIDS. Paradoxically, these concerns centre on the over-consumption of medicines of dubious benefit in Western societies, and lack of access to essential medicines in the Global South. Central questions that are explored include: what are the implications for health of existing systems of pharmaceutical drug regulation?; and what do existing systems of drug regulation reveal about the power of transnational pharmaceutical corporations to shape regulatory and other policies? The importance attached to considering the Irish regulatory system in its international context is reflected in the inclusion of chapters that address the implications of World Trade Organisation and EU regulatory policies and regulatory trends in Canada, Britain and Australia. By demonstrating how the analysis of pharmaceutical drug regulation can provide rich insights into the operation of power in contemporary society, this book challenges the prevailing construction of drug regulation as a sphere of ‘policy without politics’ and aims to contribute to the imagination of better ways of regulating medicines.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.462
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.000

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 head0.676
GPT teacher head0.552
Teacher spread0.124 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2008
Admission routes1
Has abstractyes

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