Moral experience: a framework for bioethics research
Bibliographic record
Abstract
Theoretical and empirical research in bioethics frequently focuses on ethical dilemmas or problems. This paper draws on anthropological and phenomenological sources to develop an alternative framework for bioethical enquiry that allows examination of a broader range of how the moral is experienced in the everyday lives of individuals and groups. Our account of moral experience is subjective and hermeneutic. We define moral experience as "Encompassing a person's sense that values that he or she deem important are being realised or thwarted in everyday life. This includes a person's interpretations of a lived encounter, or a set of lived encounters, that fall on spectrums of right-wrong, good-bad or just-unjust". In our conceptualisation, moral experience is not limited to situations that are heavily freighted with ethically-troubling ramifications or are sources of debate and disagreement. Important aspects of moral experience are played out in mundane and everyday settings. Moral experience provides a research framework, the scope of which extends beyond the evaluation of ethical dilemmas, processes of moral justification and decision-making, and moral distress. This broad research focus is consistent with views expressed by commentators within and beyond bioethics who have called for deeper and more sustained attention in bioethics scholarship to a wider set of concerns, experiences and issues that better captures what is ethically at stake for individuals and communities. In this paper we present our conceptualisation of moral experience, articulate its epistemological and ontological foundations and discuss opportunities for empirical bioethics research using this framework.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.010 | 0.110 |
| Scholarly communication | 0.026 | 0.031 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".