Social science and bioethics: the way forward
Bibliographic record
Abstract
It is no surprise that bioethics and sociology developed an adversarial relationship: bioethicists value the clear descriptions of ethically charged situations provided by social scientists, but doubt the ability of those trained in social science to logically derive and discern 'the good'. For their part, social scientists - experts in observing and collecting data about the way the world is- find bioethicists ignorant of the ways elegantly crafted solutions to ethical problems get altered when incarnated in varied social and cultural settings. This discipline-centred self-affirmation can be a satisfying exercise, but it offers nothing to the project of promoting more moral medicine and health care. The articles collected in this volume demonstrate the value of collaboration between social scientists and bioethicists. Focusing on four themes found in recent sociological research in bioethics - ethics of research, the creation of moral boundaries, bioethics and social policy, and the bioethical imagination - this anthology offers practical models of co-operative work where the strengths of each discipline are brought together to advance our understanding of bioethical issues and to show the way toward just and effective social policy.
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 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 | 0.044 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| 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".