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Record W2169275983 · doi:10.1177/0270467611422838

Corporate Disguises in Medical Science

2011· article· en· W2169275983 on OpenAlexaff
Sergio Sismondo

Bibliographic record

VenueBulletin of Science Technology & Society · 2011
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsQueen's University
Fundersnot available
KeywordsOpinion leadershipPharmaceutical industryPublic relationsContinuing medical educationKey (lock)Popular opinionMedical educationContinuing educationBusinessPolitical scienceEngineering ethicsPsychologySociologyMedicineEngineeringPharmacologyMedia studiesComputer science

Abstract

fetched live from OpenAlex

Roughly 40% of the sizeable medical research and literature on recently approved drugs is “ghost managed” by the pharmaceutical industry and its agents. Research is performed and articles are written by companies and their agents, though apparently independent academics serve as authors on the publications. Similarly, the industry hires academic scientists, termed key opinion leaders, to serve as its speakers and to deliver its continuing medical education courses. In the ghost management of knowledge, and its dissemination through key opinion leaders, we see the pharmaceutical industry attempting to hide or disguise the interests behind its research and education.

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)0.040
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score0.213

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.060
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0110.085
Scholarly communication0.0220.014
Open science0.0010.014
Research integrity0.0080.018
Insufficient payload (model declined to judge)0.0040.001

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 head0.449
GPT teacher head0.518
Teacher spread0.069 · 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.

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

Citations30
Published2011
Admission routes1
Has abstractyes

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