Engaging non-state armed groups on reparations
Bibliographic record
Abstract
This handbook is produced as part of the ‘Reparations, Responsibility and Victimhood in Transitional Societies’ project - a three year project funded by the Arts and Humanities Research Council (AHRC). It intends to inform Non-state Armed Group (NSAG) engagement on reparations in societies transitioning from conflict.The project examines the role of reparations in societies transitioning from conflict, paying particular attention to contested notions of victimhood and the role of non-state armed groups, civil society and donors.Although there is increasing practice and international standards on reparations, there remains a large gap in implementation on the ground.This project draws from six case studies (Colombia, Guatemala, Nepal,Northern Ireland, Peru and Uganda) and a reparations database to provide comparative analysis on the challenges on implementing reparations during and after conflict. The project team is based at Queen’s University Belfast School of Law, University of Essex, Dublin City University and Brandies University. As part of translating research findings into real world applications, this handbook aims to share some of our findings in a more accessible, user-friendly and practical output.Interviews were conducted with over 250 individuals across the six case studies, including victims, ex-fighters (state and non-state actors),civil society, reparation programme staff, CSOs and donors. The project partner is the REDRESS Trust, with collaborating organisations of the International Centre for Transitional Justice (ICTJ) and the International Organisation for Migration
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".