Bibliographic record
Abstract
Abstract Luck egalitarianism’s commitment to neutralizing brute luck inequalities is thought to imply that the elimination of disabilities is an appropriate way to eliminate the unchosen disadvantage that often accompanies disabilities. This implication is not only intuitively objectionable to some, especially those concerned with disability justice, but is subject to objections from relational egalitarians that should be taken seriously. This article defends the claim that disability elimination is not a natural implication of luck egalitarian theories of justice and that luck egalitarians can avoid the related relational egalitarian objections. I take this to be the case because luck egalitarians can consistently endorse three commitments that, together, take disability elimination off the menu of possible ways to redress the disadvantage of persons with disabilities. These three commitments are: (a) the rejection of the view that disability intrinsically makes a person worse off; (b) the endorsement of the fundamental equality of all persons; and (c) the view that luck egalitarianism advances a theory about the arrangement of social institutions.
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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.051 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".