Bibliographic record
Abstract
abstract The central issue that I consider in this paper is the use of the so‐called ‘Rule of Rescue’ in the context of resource allocation. This ‘Rule’ has played an important role in resource allocation decisions in various parts of the world. It was invoked in Ontario to overturn a decision not to fund treatment for Gaucher's Disease and it has also been used to justify resource decisions in Israel concerning the same condition. In the paper I consider the nature of the Rule of Rescue and its moral justification. The latter involves consideration of the distinction between agent‐relative and agent‐neutral obligations. If the Rule of Rescue is to be justified, it is plausible to think that it will be in the context of agent‐relative obligations. Two problems with this suggestion are considered: the role of identifiability in the Rule of Rescue and the extent to which policy makers in a health care system can be taken to have such obligations. It is argued that in both cases these problems can be overcome and hence that there is a prima facie obligation to follow the Rule of Rescue.
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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".