Who Needs to Tell the Truth? – Epistemic Injustice and Truth and Reconciliation Commissions for Minorities in Non-Transitional Societies
Bibliographic record
Abstract
Abstract Truth and Reconciliation Commissions (TRCs) have become a widely used tool to reconcile societies in the aftermath of widespread injustice or social and political conflict in a state. This article focuses on TRCs that take place in non-transitional societies in which the political and social structures, institutions, and power relations have largely remained in place since the time of injustice. Furthermore, it will focus on one particular injustice that TRCs try to address through the practice of truth-telling, namely the eradication of epistemic injustice. The article takes the Canadian and Norwegian TRCs as two examples to show that under conditions of enduring injustice, willful ignorance of the majority, and power inequality, TRCs might create a double bind for victims which makes them choose between epistemic exploitation and continued injustices based on the majority's ignorance. The article argues that the set-up and accompanying measures of TRCs are of the utmost importance if TRCs in non-transitional societies are to overcome epistemic injustice, instead of creating new relations of exploitation.
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.034 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".