Hoe te verzoenen? \nEen vergelijking tussen de truth and reconciliation commissions van Zuid-Afrika en Canada
Bibliographic record
Abstract
In dit artikel zal de Zuid Afrikaanse Truth and Reconciliation Commission (ZATRC) met de Canadese Truth and Reconciliation Commission (CTRC) worden vergeleken. Er zal worden uiteengezet in hoeverre de benadering van de definitie van waarheid invloed heeft gehad op individuele en nationale verzoening en uiteindelijk op dekolonisatie. Enerzijds zal worden onderzocht in hoeverre de CTRC, in vergelijking met de ZATRC, rekening houdt met de denkwijzen en ideeën over waarheid en verzoening van de onderdrukte Aboriginals, anderzijds zal worden onderzocht in hoeverre de CTRC, in vergelijking met de ZATRC, ondanks de victim approached-benadering van de hoorzittingen, de individuele getuigenissen van de Aboriginals in een bredere koloniale context plaatst.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.061 | 0.007 |
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".