Showing Remorse at the TRC: Towards a Constitutive Approach to Reparative Discourse
Bibliographic record
Abstract
The author argues that, despite explicit declarations by the architects of the Truth and Reconciliation Commission in South Africa expressions of remorse or apology would not constitute a requirement for amnesty, a review of the transcripts of hearings from 1996-2000 shows numerous occasions in which the persons who appeared before the tribunal gave statements to those assembled or directly to their victims in which they claimed remorse or apologized for their actions. This paper analyzes these instances of what may be called reparative discourse in terms of how they are used to mobilize feelings in support of a particular vision of community, how expectations for remorse or apology were contested or resisted by persons who had conflicting visions of community, and how participants decided whether a particular expression of remorse or an offer of apology was credible and real. The purpose of the analysis is to develop an approach to remorse and apology that shows how members decide when reparative discourse is to be expected and how this process of building expectations helps to constitute the moral boundaries of community.
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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 | 0.063 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.022 | 0.137 |
| Scholarly communication | 0.025 | 0.025 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".