Revisiting the Fact/Value Dichotomy: A Speech Act Approach to Improve the Integration of Ethics in Health Technology Assessment
Bibliographic record
Abstract
Philosophers engaged in the field of applied ethics are often challenged to revisit certain philosophical debates in order to clarify the background concepts involved in a given undertaking at stake. This is particularly evident in the field of Health Technological Assessment (HTA) where the integration of ethics has been a debate for many years. Interdisciplinary technological assessment involves a head-on discussion between the frame of reference of natural sciences and those of philosophy, which often reproduce the fact/value dichotomy debated in the field of philosophy. The challenge for a philosopher is then to explain how the fact/value dichotomy has been criticized by philosophers in such a way that the distinction between “verifiable facts” and “unverifiable values” cannot be accounted for anymore. The critiques of H. Putnam and S. E. Toulmin were the first steps towards the understanding of the dichotomy. A speech act approach, based on J. L. Austin illocutionary acts, can shed a new light on this issue by clarifying the difference between assertions, evaluations and prescriptions. By using a speech-act approach we can define the respective role of scientific evaluation and ethical evaluation in the HTA process and offer a better guide for the decision-makers on all aspects of adopting a technological development in health.
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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 | 0.128 | 0.164 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.052 |
| Scholarly communication | 0.017 | 0.032 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 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".