Bibliographic record
Abstract
In this article, I will propose a theoretical argument for the prohibition of unequal treatment of disabled and non-disabled individuals in health care resource allocation. I will first consider an argument for unequal treatment, which was put forward by Singer et al, and elucidate its far-reaching scope. I will then use the same argument in order to derive an argument that would prohibit unequal treatment of disabled and non-disabled individuals in almost all cases of health care allocation.Resumen:En este artículo propongo un argumento teórico para prohibir el trato desigual entre personas discapacitadas y no discapacitadas en la distribución de recursos médicos. En primer lugar analizaré un argumento que apoya un trato desigual, el cual fue presentado por Singer y otros, y trataré de establecer sus alcances. Después utilizaré ese mismo argumento para derivar otro que nos llevaría a prohibir el trato desigual entre discapacitados y no discapacitados en casi todos los casos de distribución de recursos médicos.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".