Evaluating Transitional Justice
Bibliographic record
Abstract
This paper examines the role of mixed and multi-level methods datasets used to inform evaluations of transitional justice mechanisms. The Colombia reparation program for victims of war is used to illustrate how a convergent design involving multiple datasets can be used to evaluate the effectiveness of a complex transitional justice mechanism. This was achieved through a unique combination of (1) macro-level analysis enabled by a global dataset of transitional justice mechanisms, in this case the reparations data gathered by the Transitional Justice Research Collaborative, (2) meso-level data gathered at the organizational level on the Unidad para las Victimas (Victims Unit), the organization in charge of implementing the reparations program and overseeing the domestic database of victims registered in the reparations program, and (3) micro-level population- based perception datasets on the Colombian reparations program collected in the Peacebuilding Data database. The methods used to define measures, access existing data, and assemble new datasets are discussed, as are some of the challenges faced by the inter-disciplinary team. The results illustrate how the use of global, domestic, and micro- level datasets together yields high quality data, with multiple perspectives permitting the use of innovative evaluation methods and the development of important findings and recommendations for transitional justice mechanisms.
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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 | 0.171 | 0.336 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".