Global Indicators for Transitional Justice and Challenges in Measurement for Policy Actors
Bibliographic record
Abstract
Indicators have become an important tool for policy actors at the bilateral and multilateral level over the past twenty years; however, they have mainly been developed in relation to development and public health goals. This note identifies the practical and methodological challenges in developing global (i.e. cross-national) indicators for transitional justice, through reflection on a practical engagement with UN Women, for which the author developed two indicators on women’s participation in truth commissions and in reparations programs. Specific challenges to developing the indicators included: the lack of administrative data on transitional justice; difficulty in establishing agreed definitions on “what” is being measured, which is linked to the lack of common agreement on the objectives of transitional justice initiatives; lack of standardization of data collection practices across countries; lack of engagement between transitional justice institutions’ staff and statisticians; and the general challenges in measuring progress against human rights objectives. I introduce a “basket” approach as an imperfect solution to this data reality. The note concludes by identifying specific changes that would ease the process of developing meaningful cross-national indicators on transitional justice.
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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 | 0.071 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.030 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".