Ethics for Rhetoric, the Rhetoric of Ethics, and Rhetorical Ethics in Health and Medicine
Bibliographic record
Abstract
Should, and could, the rhetoric of health and medicine (RHM) develop a professional disciplinary code of ethics? In this commentary, I argue that RHM has special need for a code of ethics, but that we encounter unique barriers to codification. These barriers arise not because we are not ethical, but because we are distinctively ethical. By analyzing the rhetoric of the professional disciplinary code of ethics as a genre, it becomes evident that codes have the potential to restrict a humanities field’s ethical discourse to the domain of academic research and to limit its participation in the domains of health and medicine. Subsequently, I levy that certain generic conventions of the code of ethics do not adequately meet our needs as a health humanities field. I raise, instead, the possibility of an alternative statement of ethics that better mediates the health and humanities divide. Towards the feasibility of this prospect, I begin to theorize the notion of a “rhetorical ethics”: a conceptualization of RHM as a distinctive and legitimate approach to ethical discourse in health and medicine.
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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 | 0.025 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.083 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".