Equality, Community, and the Scope of Distributive Justice: A Partial Defense of Cohen’s Vision
Bibliographic record
Abstract
Luck egalitarians equalize the outcome enjoyed by people who exemplify the same degree of distributive desert by removing the influence of luck. They also try to calibrate differential rewards according to the pattern of distributive desert. This entails that they have to decide upon, among other things, the rate of reward, i.e., a principled way of distributing rewards to groups exercising different degrees of the relevant desert. However, the problem of the choice of reward principle is a relatively and undeservedly neglected issue among luck egalitarians. The main goal of this paper is to highlight the importance and difficulty of this problem, and to elaborate upon G. A. Cohen's community-oriented response to it. In the last section, I provide a taxonomy of distributive pluralism, contrasting Cohen’s view with other (not so genuine) pluralisms - especially with all-things-considered varieties - while trying to motivate readers to adopt the more robust form of pluralism.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".