Luck, Institutions, and Global Distributive Justice
Bibliographic record
Abstract
Luck egalitarianism provides one powerful way of defending global egalitarianism. The basic luck egalitarian idea that persons ought not to be disadvantaged compared to others on account of his or her bad luck seems to extend naturally to the global arena, where random factors such as persons’ place of birth and the natural distribution of the world’s resources do affect differentially their life chances. Yet luck egalitarianism as an ideal, as well as its global application, has come under severe criticisms in recent debate. My aim in this article is to restore plausibility to the luck egalitarian idea, and to suggest how it could then provide a plausible grounding for global egalitarianism. To do this, I will propose a more modest but also more defensible conception of luck egalitarianism that can also strengthen the case for global distributive justice
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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 | 0.009 | 0.007 |
| 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 | 0.045 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".