Truth commissions in the established democracies of the Global North: Theoretical and practical perspectives
Bibliographic record
Abstract
Abstract In the wake of South Africa's truth‐telling experiment as part of its transition from apartheid to democracy, truth commissions have become one of the most utilized mechanisms for addressing past atrocities. While most truth commissions are established in countries undergoing “transition” to democratic governance or peace, increasingly, established democracies such as Canada, Norway, Sweden, and Finland have also undertaken such processes to address historical (and often, racial) injustices. The U.S. Department of State has denied the relevance of truth commissions to the United States for addressing its own history of racial injustice, however, the U.S. itself has been home to at least 13 official truth commissions (operating primarily at the state‐, county‐, and city‐level) and numerous unofficial truth‐telling processes emanating from civil society. In this article, I review literature on truth commissions with a focus on history and theorized importance, recent application to the more established democracies of the “Global North” and overall significance and limitations in terms of fostering racial justice and social transformation in what are primarily settler colonial states. I conclude by evaluating the state of this research area and by suggesting directions for future research.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.078 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".