Bibliographic record
Abstract
Reconciliation is one of the most contested concepts in the scholarly debate on transitional justice, and arguably also the most difficult to measure empirically.This article provides an overview and assessment of current knowledge on the relationships between transitional justice mechanisms aimed at promoting "truth" and "justice" and the (end) goal of "reconciliation" in its multiple forms.It first spells out the claims about how to foster reconciliation and about how different mechanisms such as truth commissions, trials, amnesties, and local justice initiatives can be expected to contribute toward this end goal.Next, it takes stock of singlecase, comparative and broad sample impact studies of reconciliation processes.It finds that reconciliation may be most usefully studied as a process rather than as a goal, and that more attention should be given to the interplay between formal and local transitional justice processes.The article concludes that methodological challenges for cross-country analysis include (1)
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".