Truth Commissions in Non-Transitional Contexts: Implications for Their Impact
Bibliographic record
Abstract
This chapter examines the reasons why TCs have emerged in these other kinds of contexts and the ways in which TC expectations might need to be altered in these different environments. First, it examines the circumstances under which TCs are created. Second, it reviews prevailing expectations about the impact of TCs, noting that there is usually an assumption that they are created in transitional contexts where social, political, and economic institutions are undergoing significant upheaval. Third, it explores how TCs often differ when they are employed in contexts where little to no transformation is taking place. Specifically, it examines TCs that have been created in post-conflict scenarios, such as Sri Lanka, where the government decisively defeated an armed threat, so there is no real political transition. It also considers Bahrain and Morocco, where firmly entrenched authoritarian regimes created TCs to address histories of human rights violations. In addition, cases such as Canada’s TRC and the Maine-Wabanaki TRC represent examples of the model being utilized in well-established democracies in order to address historical wrongs. The chapter concludes by reflecting upon the conceptual and empirical consequences with respect to setting expectations as to the legacies of TCs in such contexts. The creation of these bodies are at least in part a consequence of a global TC norm. While these examples might be dismissed as costless, cynical attempts to exploit a global truth-seeking norm, the public nature of these bodies ensures that they draw the world’s attention to a government’s response to a TC’s work.
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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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.046 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".