A Survey of Attitudes Toward Social Justice Obligations in the Field of Bioethics
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.168 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.011 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".