Luck, Love, and Extreme Skiing: Distributive Injustice without Unfairness
Bibliographic record
Abstract
Abstract In the classic article, “On the Currency of Egalitarian Justice,” G.A. Cohen states that “a large part of the fundamental egalitarian aim is to extinguish the effects of brute luck on distribution.” This canonical formulation pinpoints what is distinctive of the luck-egalitarian mandate. But it also indicates that that mandate, so stated, is incomplete. The primary task of the paper is to extend what is explicit within that mandate, and in doing so, to bring it closer to completion. To that end, I defend – in the spirit of Cohen, and by arguments he pioneered – a new, expanded conception of luck-egalitarian compensation. I propose, accordingly, an amendment, seemingly friendly, to Cohen’s statement. But, in fact, my proposed amendment, and its rationale, reveal a major lacuna in the normative underpinnings of Cohen-style egalitarianism. I thereby show that, contrary to what is widely assumed, important foundational work remains to be done for the luck-egalitarian project.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".