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Record W3093255292 · doi:10.1111/bioe.12820

The value of communities and their consent: A communitarian justification of community consent in medical research

2020· article· en· W3093255292 on OpenAlexaff
Pepijn Al

Bibliographic record

VenueBioethics · 2020
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsWestern University
Fundersnot available
KeywordsAutonomyInformed consentBioethicsPublic relationsValue (mathematics)Community engagementPolitical sciencePsychologySociologyLawMedicineAlternative medicine

Abstract

fetched live from OpenAlex

Community engagement is increasingly defended as an ethical requirement for biomedical research. Some forms of community engagement involve asking the consent of community leaders prior to seeking informed consent from community members. Although community consent does not replace individual consent, it could problematically restrict the autonomy of community members by precluding them from research when community leaders withhold their permission. Community consent is therefore at odds with one of the central principles of bioethics: respecting autonomy. This raises the question as to how community consent can be justified or even required. This paper aims to provide an answer to this question by arguing, based on the work of Taylor and Kymlicka, that community practices are important for the identity and autonomy of community members. When these practices are incompatible with a solitary focus on individual informed consent, they need to be protected by making these decision-making practices (including asking permission to community authorities) part of the consent process. Since these decision-making practices are important for the autonomy of community members, community consent with the goal of protecting these practices is not necessarily in conflict with autonomy.

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)0.211
metaresearch head score (Gemma)0.189
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.211
Threshold uncertainty score0.972

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2110.189
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0140.156
Scholarly communication0.0140.022
Open science0.0040.023
Research integrity0.0230.022
Insufficient payload (model declined to judge)0.0020.001

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 head0.715
GPT teacher head0.614
Teacher spread0.101 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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