Between Groups and Across Time: A Relational Group-based Account of Reparations for Historical Injustice
Bibliographic record
Abstract
Many people believe that injustices committed long ago ought to be repaired. Yet, the possibility of reparative justice for historical injustices encounters significant philosophical objections. In particular, it appears as though we cannot apply the standard picture of reparative justice that we use for most injustices. On this picture, reparative justice requires that the wrongdoer provide reparations to the victim, the content of which corresponds to the way the wrongdoer’s action was unjust. This standard picture captures what we want from an account of reparative justice: The wrongdoer ought to be accountable to the victim and address the victim’s losses caused by the wrongdoer’s action. However, we cannot seem to extend this intuitive standard picture to cases of historical injustice. The individual wrongdoers and victims no longer exist, often the content of the injustice is not something that we could possibly repair, and the effects of the injustice are so widespread that it seems impossible to determine which effects ought to be repaired. These challenges have led many philosophers to argue that we cannot extend the standard picture of reparative justice to cases of historical injustice. They argue for alternative models of what ought to be done to address historical injustice based on structural injustice, the beneficiary pays principle, or equal distributions. While these models do something for historical injustice, they do not capture what we seem to want from reparative justice: The wrongdoer ought to do something for the victim to take responsibility for their wrong. This means that the alternative models cannot capture what we seem to want from an ideal account of reparations for historical injustice. My goal is to show that it is possible to extend the standard picture to cases of historical injustice. I develop the Relational Group-Based Account of reparative justice for historical injustice. The central point of this account is that reparations for historical injustice are about what one group wrongfully did to another. I defend this account by demonstrating how it overcomes major philosophical objections against the possibility of reparations and by resolving important challenges about group wrongs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.006 | 0.018 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".