Introducing the Transitional Justice Evaluation Tools (TJET) database
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The TJET project offers a comprehensive database for exploring the supply of transitional justice (TJ) in every country of the world. TJET provides detailed descriptive information on domestic, foreign, and international prosecutions; truth commissions; reparations policies; vetting policies; amnesty laws and offers; and UN investigations. This article describes TJET’s quantitative dataset, consisting of longitudinal data from 1970 to 2020, with over 400 measures related to the design and operation of TJ mechanisms. Because TJ has become integral to discussions related to democracy and rule of law promotion, as well as peacebuilding, it is necessary that researchers and practitioners use the most comprehensive information possible for grounding their analysis and advocacy. The TJET dataset is unique not only in its global coverage, but also in its custom sampling feature, allowing users to select which types of cases to compare. This article provides descriptive data on TJ attributes, analysis of new trends, and an examination of the temporal relationship between different TJ mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it