WHO ARE WE WHEN WE ARE DOING WHAT WE ARE DOING? THE CASE FOR MINDFUL EMBODIMENT IN ETHICS CASE CONSULTATION
Bibliographic record
Abstract
This paper explores the theory and practice of embodied epistemology or mindful embodiment in ethics case consultation. I argue that not only is this epistemology an ethical imperative to safeguard the integrity of this emerging profession, but that it has the potential to improve the quality of ethics consultation (EC). It also has implications for how ethics consultants are trained and how consultation services are organized. My viewpoint is informed by ethnographic research and by my experimental application of mindful embodiment to the development of an ethics consultation service. My argument proceeds in four phases. First I explore the notion of 'situatedness' in the bioethics literature, identifying gaps in the field's theories as they apply to EC. I then describe my theoretical approach to embodiment grounded in critical-interpretive medical anthropology and autoethnography. I use embodiment to refer to a moral epistemology grounded in the body, comprised of the interplay of physical, symbolic, intersubjective and political elements. Third, I describe how mindful embodiment can inform the role of the ethics consultant and the development of effective training techniques, vocabularies and processes for EC. I also discuss the benefits of this orientation, and the potential harms of ignoring the embodied dimensions of EC. My goals are to expose the fallacy of the 'theory-practice gap', to demonstrate how my own EC practice is deeply informed by this theoretical orientation, and to argue for a wider definition of what 'counts' as relevant theory for ethics consultation.
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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 | 0.055 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.121 |
| Scholarly communication | 0.016 | 0.030 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.017 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 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".