How Does a Truth Commission Find out What the Truth Is? The Case of East Timor's Cavr
Bibliographic record
Abstract
has a formidable name to live up to, especially in this age when many people dare not use the word outside of quotation marks. The name itself proclaims a faith in the possibility of finding out the about past events. It was the Chilean government that first began the practice of using the term when it established a body in 1990 to investigate the human committed by the Pinochet regime. The term set a trend for the 1990s as the governments of El Salvador, Haiti and South Africa, among others, also put the term truth into the names of their commissions for human rights investigations. If the government officials who invoked the term reflected on how philosophically loaded and intractable it is, they might have stuck with the customary, bland appellation commission of inquiry. As it was, the members of these various commissions were saddled with the burden of trying to figure out the meaning of the term and how one might go about investigating it. The South African Truth and Reconciliation Commission (TRC), after much debate, concluded that it was working on four types of simultaneously: forensic, narrative, social and restorative a listing that appeared to some analysts as a haphazard jumble of disparate, even antinomous, concepts.1 In this article, I will examine what kind of East Timor's commission, the CAVR (Comissao de Acolhimento, Verdade e Reconciliacao), decided to investigate and what kind of evidence it adduced in its final report to support its claims.2 .The CAVR, tasked by the government of East Timor with establishing the regarding past human rights violations and presenting factual and objective information, had to decide how it would go about fulfilling that mandate.3 As the most recent
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.038 | 0.061 |
| Scholarly communication | 0.036 | 0.034 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.019 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 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".