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Record W6909131731 · doi:10.34880/nn1y-3j33

Performance and Perception: The Impact of the Extraordinary Chambers in the Court of Cambodia

2021· report· en· W6909131731 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Society Foundations · 2021
Typereport
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsGenocideEconomic JusticePoliticsReignDemocracyAccountabilityQuarter (Canadian coin)

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.752
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.078
GPT teacher head0.390
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueOpen Society FoundationsFrench-language works237,207