Komisi Kebenaran dan Rekonsiliasi dalam Era Nontransisional: Implementasi di Korea Selatan dan Kanada
Bibliographic record
Abstract
This article aims to explain the practice of Truth and Reconciliation Commission (TRC) in non-transitional era. The TRC in non-transitional era was formed by democratic country or to reveal the truth of gross human rights violations that occurred decades ago. This research uses comparative method that compares the practice of TRC in South Korea (Commission on Clearing up Past Incidents for Truth and Reconciliation/TRCK) and Canada (Truth and Reconciliation Commission of Canada/TRCC). The results of the study indicate that the TRCK and TRCC were formed as an effort by the state to improve previous efforts in dealing with gross human rights violations; the number of staff members had a more significant impact on the success of the TRC than the number of commissioners; the norms governing the protection, prohibition, and sanctions for commissioners and staff, testifying witnesses, the persons named in the testimony and for individual and community; TRCK and TRCC gathered facts within two years; and the reconciliation process was carried out by the commission through the rehabilitation of reputations and holding memorial services. This article recommends that the practice of TRC in South Korea and Canada can be adopted in the preparation of policies for the establishment of TRCs in Indonesia.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".