Bibliographic record
Abstract
Compensation for historic wrongs was once legally unthinkable. Now such claims are increasingly commonplace and count among law’s most difficult cases. This article tells the story of how historic wrongs became legal problems and seeks to provide the foundations for a more robust understanding of redress. To date, literature on historic injustice has tended to focus on threshold questions or on the relatively novel terrain of truth commissions, acknowledgement, and commemoration. The survivor’s quest for individual redress has, by contrast, garnered relatively little sustained attention even though such claims are among the most disruptive and challenging aspect of this ‘new’ problem of historic wrongs. This project aims to respond to that gap. It begins by seeking to better understand the problem, using three illustrative cases to help trace how historic wrongs came to be among law’s most vexing problems of responsibility. The UK decisions on the Mau Mau uprising highlight how claims that seek redress for historic wrongs often exhibit surprising force, capable of eroding the once-powerful procedural rules that used to protect the past from legal responsibility. Canada’s five-billion-dollar settlement of claims relating to the legacy of Indian residential school reminds us both of this force and of the challenges law faces when confronted by its own complicity in historic injustice. Finally, the US reparations for the slavery movement illuminates how, despite defeat in the courtroom, ‘reparative justice’ claims often possess a tenacious quality that makes them capable of moving powerful institutions. Tracing the role law has played in spurring the problem of the past helps to illuminate some of the key features of redress that have to date been all but ignored and provides the basis for developing more effective responses to historic wrongs.
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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.084 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.011 | 0.016 |
| 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".