Reparation for Victims of Crimes against Humanity
Bibliographic record
Abstract
Introduction, Jo-Anne M. Wemmers Part 1. The Victims 1. Healing Aspects of Reparations for Victims of Crimes against Humanity, Yael Danieli 2. Reparation and Recovery in the Aftermath of Widespread Violence, Christophe Herbert, Charlie Rioux and Jo-Anne M. Wemmers 3. Restoring Justice for Victims of Crimes against Humanity, Jo-Anne M. Wemmers Part 2. Victims and the Law 4. Reparative Justice at the International Criminal Court: Best practice or tokenism?, Mariana Goetz 5. It Doesn't Go Away with Time: Victims' need for reparation following crimes against humanity, Amissi M. Manirabona and Jo-Anne M. Wemmers 6. The Prosecute of Expel Dilemma in Far-Away Lands: Alternative universal justice for victims of international crimes, Fannie Lafontaine Part 3. Victims and Society 7. Framing Reparation Claims for Crimes against Humanity: A social-psychological perspective, Katherine Starzyk, Danielle Gaucher, Gregory Boese and Katelin Neufeld 8. The Healing and State? Residential schools and reparations in Canada, Andrew Woolford 9. Transitional Justice in Bosnia-Herzegovina: Understanding accountability, reparation and justice for victims, Nicholas A. Jones, Stephan Parmentier and Elmar G. M. Weitekamp 10. The Art of Acknowledgement: Re-imagining relationships in Northern Ireland, Jill Strauss Part 4. Collective Reparation and the Law 11. The Case for Collective Reparations before the International Criminal Court, Frederic Megret 12. Land, Wars and Restoring Justice for Victims, Gabriela Manrique Rueda 13. Reparations Through Different Lenses: The culture, rights and politics of healing and empowerment after mass atrocities, Hugo Van Der Merwe Part 5. Conclusion 14. The Healing Role of Reparation, Jo-Anne M. Wemmers.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.051 | 0.014 |
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".