MétaCan
Menu
← Back to cohort
Record W2003101967 · doi:10.1177/0008429813513234

Negotiating Virtue

2014· article· en· W2003101967 on OpenAlexaffvenue
Amyn B. Sajoo

Bibliographic record

VenueStudies in Religion/Sciences Religieuses · 2014
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsSimon Fraser University
FundersU.S. Department of Energy
KeywordsVirtueBioethicsLegalism (Western philosophy)Virtue ethicsAutonomySociologyContext (archaeology)HonourNegotiationEnvironmental ethicsEpistemologyLawPolitical sciencePhilosophySocial sciencePolitics

Abstract

fetched live from OpenAlex

Principlist modes of reasoning in bioethics – with autonomy at the core – resonate strongly with a legalism that dominates Muslim ethics, including the understanding of the shari’a. From abortion and organ donation/transplant to end-of-life decisions, both secular and Muslim bioethics generally apply “cardinal” principles in ways felt to be relatively objective and certain, though they may produce different outcomes. This article builds on recent critiques, notably that of virtue ethics, in drawing attention to the cost in sensitivity to context and the individual. The Aristotelian basis of virtue ethics has a venerable place in Islamic traditions – as does maslaha, the public good, which has long played a critical role in tempering formalism in the shari’a. In conjunction with the agent- and context-centred reasoning of virtue ethics, maslaha can contribute vitally to negotiating competing bioethical claims. It is also more inclusive than principlist legalism, given the latter’s traditionalist and patriarchal moorings. The shift is urgent amid the growing interface of religious and secular approaches to problems raised by biomedical technologies, and to biosocial issues such as female genital mutilation (FGM) and honour killings.

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)0.022
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.022
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.034
Scholarly communication0.0100.013
Open science0.0020.013
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0100.002

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 head0.061
GPT teacher head0.391
Teacher spread0.331 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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 routes2
Has abstractyes

Explore more

Same venueStudies in Religion/Sciences Religieuses→Same topicOrgan Donation and Transplantation→French-language works237,207→