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Record W55583113

The Markingson Case and the Ethics of Institutional Proceduralism

2014· article· en· W55583113 on OpenAlexaff
Trudo Lemmens

Bibliographic record

VenueSSRN Electronic Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCoercion (linguistics)Undue influencePolitical scienceResearch ethicsClinical trialPublic relationsEngineering ethicsPsychologyMedicineMedical educationLawPsychiatryEngineering
DOInot available

Abstract

fetched live from OpenAlex

This article discusses the controversy surrounding the 2004 suicide of Dan Markingson in a clinical trial at a University of Minnesota hospital. It explains the rationale behind a recent initiative by a group of more than 170 international scholars to request a publicly accountable inquiry into the events surrounding the suicide and into the lack of proper investigation by the University and several other agencies (including the FDA and professional regulators) mandated to protect patients and research subjects. The case raises serious and ongoing concerns about the enrollment of vulnerable psychiatric patients in research, coercion and undue influence, the impact of conflicts of interest on the behavior of clinical investigators and university administrators, the qualifications of research personnel, and the integrity of medical research at major medical schools. The arguments invoked by various institutional players to reject a further investigation shows how existing research ethics review procedures may result in superficial assessments that can easily be employed as a protective shield. Instead of promoting better ethical standards in research, they may sometimes become tools to hide abuse.

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)0.079
metaresearch head score (Gemma)0.111
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score0.417

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0790.111
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0150.081
Scholarly communication0.0140.011
Open science0.0020.011
Research integrity0.0210.019
Insufficient payload (model declined to judge)0.0030.001

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 head0.138
GPT teacher head0.483
Teacher spread0.345 · 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 designTheoretical or conceptual
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
Published2014
Admission routes1
Has abstractyes

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