Bibliographic record
Abstract
Abstract Not long ago, complaints about old wrongs were dismissed with statements like “time heals all wounds” or “let sleeping dogs lie.” Survivors of assault, rape, torture, and looting were told that it was just too late. But now the problems of the past are coming back to haunt us. Institutions face complex reckonings with what their forebearers did, and they struggle to make amends. From the Holocaust to child abuse to colonialism, from looting to eugenics to slavery, demands to rectify old wrongs pose unsettling modern challenges. This book traces how the past became such a daunting contemporary problem. It shows how innovative private law actions for redress of old wrongs began to draw on the idea of reparative justice, using it to reshape traditional legal doctrines such as reparations and restitution. As reparations for old injuries and restitution of belongings taken long ago became increasingly common, transitional justice inspired remedies, such as truth commission style bodies and broader remedies, began to appear in stable democracies. In addition to tracing how the past became such a current problem, this book also seeks to draw some useful lessons for institutions seeking to make amends for historic wrongs. It addresses some of the most common challenges of these cases, many of which revolve around lawyers. It also explores some of the most positive lessons, illustrating how engagement with survivors and descendants can reap benefits, not only for them but also for the larger communities seeking to make amends.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".