What Procedural Ethics Can Learn from the Quest for Moral Justification for the “Rule of Rescue”
Bibliographic record
Abstract
In this issue, Sirrs and colleagues (2023) provide a very informative picture of the value and cost of policies to promote orphan drug development. They examine the influence of these policies on pharmaceutical research and development, the proliferation of rare diseases, the prohibitive costs and the loopholes of these policies. One section of the paper identifies the ethical issues and proposes a response to the challenge of integrating the utility perspectives of pharmacoeconomic analyses with those of treatment access claims formulated from a deontological perspective. Their proposal is essentially that of procedural ethics. I enter the ethical debate obliquely by looking at the rule of rescue phenomenon observed by Albert R. Jonsen (Jonsen 1986). I explore the twists and turns of the discussion on this subject and assume the perspective of authors who give significant weight to the symbolic value of respecting it. In conclusion, I take up the symbolic question by arguing that the challenge of preserving the aura of legitimacy that must surround political decisions sometimes requires distancing oneself from sound recommendations, which, even if they are the result of an ideal procedure, will nevertheless be perceived as unjust and insensitive.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.054 | 0.054 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".