On the necessity of a pluralist theory of reparations for historical injustice
Bibliographic record
Abstract
Abstract Philosophers have offered many arguments to explain why historical injustices require reparations. This paper raises an unnoticed challenge for almost all of them. Most theories of reparations attempt to meet two intuitions: (1) reparations are owed for a past wrong and (2) the content of reparations must reflect the historical injustice. I argue that necessarily no monistic theory can meet both intuitions. I do this by showing that any theory that can meet intuition (1) necessarily cannot also meet intuition (2). This result suggests that a theory of reparations must either sacrifice one of the two intuitions or be pluralist. I argue that we ought to prefer the pluralist theory over the monistic theory. I sketch the pluralist theory, and I defend it by considering an objection about the way it can resolve conflicts.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.053 |
| Scholarly communication | 0.009 | 0.021 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".