Trading Justice for Peace? Reframing reconciliation in TRC processes in South Africa, Canada and Nordic countries
Bibliographic record
Abstract
Conflict in its various manifestations continues to be a defining feature in many places throughout the world. In an attempt to address such conflict, various forms of a Truth and Reconciliation Commission (TRC) have been introduced to facilitate the transition from social conflict to a new dispensation. The introduction and subsequent proceedings of TRCs in South Africa, Canada and Norway are widely regarded as good examples of this approach. Against this background, a number of researchers from VID Specialized University and the University of the Western Cape had an exploratory meeting in Oslo in 2018 where the possibility for a joint research project under the broad theme of ‘discourses on reconciliation’ was first discussed. This led to two further research symposia in Cape Town and Tromsø in 2019. With the inclusion of specialists working on the Canadian Truth and Reconciliation process, these meetings demonstrated common ground and a shared understanding of the issues at stake. Moreover, it pointed to the differences between the South African, Canadian and Norwegian Commissions. In comparing the South African, Canadian and Norwegian experiences, researchers identified that these countries were, in fact, at different stages of their respective truth and reconciliation processes. This has prompted scholars to revisit and problematise these processes in relation to ongoing societal challenges. In all cases, it is quite apparent that reconciliation between individuals and groups remains a significant challenge.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.053 | 0.045 |
| Scholarly communication | 0.027 | 0.009 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".