Bibliographic record
Abstract
ABSTRACT Reparative justice for historical injustice concerns what present agents and societies must do to remedy past wrongs. Examples of historical injustice include the Holocaust, colonial violence and land expropriations, and chattel slavery in the United States. There is widespread intuition that these kinds of past wrongs require some form of reparation. However, because of the time that has passed between past wrongs and the present, explaining why reparative justice for these wrongs is possible encounters philosophical issues, including the nonidentity problem, the supersession thesis, issues of causal indeterminacy, question of personal identity, and questions of group responsibility. These issues have created many debates among philosophers about the ideal account that can best explain how reparative justice for historical injustice is possible. In this paper, I carve up the conceptual terrain of these debates. I present the different positions one could take for each major philosophical issue concerning historical injustice, and I discuss their merits and drawbacks. To conclude, I sketch an account that offers what I take to be the most promising way forward for an account of reparations for historical injustice that best incorporates the insights from these debates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.060 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.011 | 0.013 |
| 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".