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Record W344148760

Ignorance Is Neither Bliss nor Ethical

2007· article· en· W344148760 on OpenAlexaboutno aff
John H. Mueller

Bibliographic record

VenueNorthwestern University law review · 2007
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsnot available
Fundersnot available
KeywordsIgnoranceContext (archaeology)LawMandateLiabilitySociologyPoliticsPolitical scienceHistory
DOInot available

Abstract

fetched live from OpenAlex

The whole aim of practical politics is to keep the populace alarmed (and hence clamorous to be led to safety) by menacing it with an endless series of hobgoblins, most of them imaginary. - H. L. Mencken[dagger] CONTEXT It is a common ritual among today's academics to submit research proposals to a group of colleagues, the Institutional Review Board (IRB; REB in Canada). When IRBs were first created in 1974,1 they were directed to assess whether a researcher's proposed project would expose the public to greater than everyday Over the last thirty years, IRBs have evolved to review research proposals against criteria well beyond the scope of their original mandate or their ostensible purpose.2 For example, instead of assessing whether proposed research would expose the public to greater than everyday IRBs now ask whether proposed research would expose members of the public to even risk. In practice minimal risk is a euphemism for zero risk, which is an impossible objective to achieve.3 Likewise, IRBs have expanded the review process beyond the issue of publie safety to pursue more nebulous agendas, including whether the proposed research is worthwhile. They now weigh expected social benefits, legal liability, and similar issues, presuming to judge at the outset that which can only be determined by examining the results. The broadening scope of IRB inquiry can charitably be described as creep,4 and because this expansion has happened in small steps over thirty years, the successive impositions were seldom challenged. However, the cumulative effect is striking,5 and there is no sign this mission creep has been stalled. Such subterfuge is typical of the way the research ethics enterprise has expanded over the years, always with the result that control of inquiry is increased with no documented evidence of enhanced subject safety. Today, particularly in the field of non-medical research, the institutional review process is more accurately described as censorship than safety screening.6 My intent here is to (1) describe how various distortions are used to defend and justify the ethics reviews, and (2) highlight some costs of the ethics enterprise that are routinely ignored. I will focus on social science and humanities research, in part because of my interests, but also because these disciplines seem most vulnerable to unwarranted censorship. When all of the results, intended and otherwise, are considered, it is clear that the constraints imposed on academic inquiry have not been accompanied by an increase in public benefits. I. BENEFITS The benefits of IRBs can be divided into two sets. The first set includes benefits IRB supporters claim accrue to the public despite the lack of reliable evidence that such benefits have materialized. My attention to these will be mainly to critique the shortcomings of the claim that procedure X improves public safety. The second set will concern benefits that accrue exclusively to regulators. These benefits are far more obvious, though they remain unacknowledged by IRB supporters. A. What is the Evidence, and How Do We Evaluate It Before I analyze the claimed benefits and the actual benefits to regulators, it is important to establish a clear understanding of what constitutes reliable evidence that can support a claim that IREs produce certain benefits. By definition, identifying something as a requires some variation of a pre-post assessment. That is, one needs the identification of a null or undesirable state to begin with, a manipulation, and then a secondary assessment that documents improvement. The former shows evidence of a need, and the latter shows evidence of the effectiveness of the manipulation, in this case the ethics review. The general claimed benefit is improved public safety, and I think the burden of proof for that is on the claimant. What is the evidence and what is its validity? …

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.001

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 head0.269
GPT teacher head0.503
Teacher spread0.234 · 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
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

Citations32
Published2007
Admission routes1
Has abstractyes

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