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Record W4409553494 · doi:10.1080/23294515.2025.2474915

A Survey of Attitudes Toward Social Justice Obligations in the Field of Bioethics

2025· article· en· W4409553494 on OpenAlexaffabout
Danielle Pacia, Sana S. Baban, Faith E. Fletcher, Zamina Mithani, Jane Cooper, J. Wesley Boyd, Ryan J. Dougherty

Bibliographic record

VenueAJOB Empirical Bioethics · 2025
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBioethicsField (mathematics)Economic JusticePsychologySocial psychologySociologyEngineering ethicsPolitical sciencePublic relationsLaw

Abstract

fetched live from OpenAlex

This study examines the views of bioethicists in the US and Canada on incorporating social justice into their work and the field more broadly. Through an iterative process with leaders in bioethics, we created a survey and distributed it via bioethics listservs and individual emails. Ultimately, we received responses from 355 bioethicists in the US and Canada. Respondents showed strong support for integrating social justice concerns, with 80% endorsing its inclusion in bioethics and 75% believing it should be a key aim of bioethics scholarship. However, engagement with specific social justice topics varied, and perceptions about institutional support for doing so were mixed. Early-career bioethicists were more likely to support integrating social justice into bioethics. Our findings highlight the importance of prioritizing social justice within bioethics and underscore the need for institutional support to advance these efforts.

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)0.012
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.448

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.003
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.002
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 head0.461
GPT teacher head0.638
Teacher spread0.176 · 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 designObservational
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

Citations3
Published2025
Admission routes2
Has abstractyes

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