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The Ethical Analysis of Risk

2000· review· en· W2027177214 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Journal of Law Medicine & Ethics · 2000
Typereview
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health ResearchDalhousie University
KeywordsInformed consentInstitutional review boardConfidentialityResearch ethicsBalance (ability)Ethical issuesEngineering ethicsSubject (documents)PsychologyTask (project management)Actuarial scienceMedicineRisk analysis (engineering)Political scienceBusinessAlternative medicineComputer scienceLawEngineeringPsychiatry

Abstract

fetched live from OpenAlex

The institutional review board (IRB) is the social-oversight mechanism charged with protecting research subjects. Performing this task competently requires that the IRB scrutinize informed-consent procedures, the balance of risks and potential benefits, and subject-selection procedures in research protocols. Unfortunately, it may be said that IRBs are spending too much time editing informed-consent forms and too little time analyzing the risks and potential benefits posed by research. This time mismanagement is clearly reflected in the research ethics literature. A review of articles published between 1979 and 1990 in IRB: A Review of Human Subjects Research , for example, reveals a large number of articles on informed consent and confidentiality (142 articles) and considerably fewer on the assessment of risks and potential harms (40), study design (20), and subject-selection procedures (5).

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.

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)0.147
metaresearch head score (Gemma)0.202
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.861
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1470.202
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0010.002
Science and technology studies0.0010.008
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0050.084
Insufficient payload (model declined to judge)0.0010.000

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 head0.720
GPT teacher head0.683
Teacher spread0.038 · 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