Truth and reconciliation in Southeast Asia and the Melanesian Pacific: Potential Canadian contributions and potential lessons for Canada
Bibliographic record
Abstract
Recent experiences with truth and reconciliation processes in Southeast Asia and the Southwest Pacific suggest that there is a role for historical research and memory in helping to build sustainable peace and stability in new nations—and conversely, that ignoring violent pasts undermines peacebuilding efforts. Two truth commissions have operated in this region, in Timor-Leste (East Timor) and Solomon Islands. There are also calls for truth and reconciliation processes in Indonesia at the national and local levels, including in (West) Papua. As the only Western developed country to have held a full truth commission, Canada could play a powerful role in promoting and supporting mutual dialogue on the implementation of truth and reconciliation outside its borders. We can derive both potential lessons and recommendations for Canadian action to promote truth and reconciliation processes from the cases of Indonesia, Solomon Islands, and Timor-Leste.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.028 | 0.011 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".