Towards an ethics of authentic practice
Bibliographic record
Abstract
This essay asks how we might best elaborate an ethics of authentic practice. Will we be able to agree on a set of shared terms through which ethical practice will be understood? How will we define ethics and the subject's relation to authoritative structures of power and knowledge? We begin by further clarifying our critique of evidence-based medicine (EBM), reflecting on the intimate relation between theory and practice. We challenge the charge that our position amounts to no more than 'subjectivism' and 'antiauthoritarian' theory. We argue that an ethical practice ought to question the authority of EBM without falling into the trap of dogmatic antiauthoritarianism. In this, we take up the work of Hannah Arendt, who offers terms to help understand our difficult political relation to authority in an authentic ethical practice. We continue with a discussion of Michel Foucault's use of 'free speech' or parrhesia, which he adopts from Ancient Greek philosophy. Foucault demonstrates that authentic ethical practice demands that we 'speak truth to power.' We conclude with a consideration of recent biotechnologies, and suggest that these biomedical practices force us to re-evaluate our theoretical understanding of the ethical subject. We believe that we are at a crucial juncture: we must develop an ethics of authentic practice that will be commensurable with new and emergent biomedical subjectivities.
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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 | 0.046 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.052 |
| Scholarly communication | 0.013 | 0.029 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".