Performance and Perception: The Impact of the Extraordinary Chambers in the Court of Cambodia
Bibliographic record
Abstract
When the forces of the Party of Democratic Kampuchea―the Khmer Rouge―seized Cambodia’s capital Phnom Penh in April 1975, they began a reign of terror that brought death to nearly a quarter of the country’s eight million people, and added a new chapter to the 20th century’s grim history of mass atrocities. The Extraordinary Chambers in the Courts of Cambodia (ECCC) is tasked with bringing to trial those responsible for the war crimes, crimes against humanity, and genocide committed by the Khmer Rouge regime between April 1975 and December 1979. This report grapples with all the complexity, promise, and shortcomings of the ECCC, which began operations in 2007. Its prosecution of the top surviving Khmer Rouge leaders has been called the biggest war crimes trial since Nuremberg. Yet the tribunal’s operations have been dogged by allegations of political interference, and complaints over the slow pace and the costs of the proceedings. The Open Society Justice Initiative has regularly monitored and reported on events at the tribunal. Drawing on that accumulated knowledge, this report’s first half assesses the court’s efforts to provide accountability for Khmer Rouge crimes, and its broader impact on the development of the rule of law in Cambodia. The report’s second half looks at how the intended beneficiaries of the court feel about its work. Based on dozens of interviews with court staff, ordinary Cambodians, and even former members of the Khmer Rouge, Performance and Perception offers the most comprehensive assessment to date of what the ECCC has done, and what Cambodians think of it.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".