The Quantitative Turn in Transitional Justice Research
Bibliographic record
Abstract
In recent years, scholars have increasingly turned to quantitative research methods to understand the impact of transitional justice (TJ) on societies emerging from periods of violence and repression. This research often seeks to influence policy diffusion by making bold claims based upon large datasets of TJ events that span space and time. However, the policy advice from the first wave of quantitative research is inconsistent if not contradictory. In this article, we outline a range of methodological issues that help to explain the different conclusions reached by these studies, including sampling strategies, model construction, and the measurement of key variables. Furthermore, these studies have often failed to provide compelling theoretical or empirical bases for a causal relationship between TJ mechanisms and dependent variables such as democracy and human rights. We suggest several ways in which quantitative scholars could produce findings with broader credibility. Although we support the use of quantitative methods to understand the impact of TJ mechanisms, greater methodological care is needed in supporting policy recommendations.
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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 | 0.096 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.003 | 0.036 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.010 |
| 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".