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Record W2895605838 · doi:10.1177/186810341803700204

Neglecting Social and Economic Rights Violations in Transitional Justice: Long-Term Effects on Accountability: Empirical Findings from the Extraordinary Chambers in the Courts of Cambodia

2018· article· en· W2895605838 on OpenAlexfundno aff
Tine Destrooper

Bibliographic record

VenueJournal of Current Southeast Asian Affairs · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCambodian History and Society
Canadian institutionsnot available
FundersEnvironment and Climate Change Canada
KeywordsHuman rightsAccountabilityTransitional justiceMandatePoliticsLawEconomic JusticeCultural rightsPolitical scienceInternational human rights lawSociology

Abstract

fetched live from OpenAlex

This article builds on theories about the expressive function of law and uses Structural Topic Modelling to examine how the prioritisation of civil and political rights (CPR) issues by the Extraordinary Chambers in the Courts of Cambodia (ECCC) has affected the agendas of Cambodian human rights NGOs with an international profile. It asks whether these NGOs’ focus on CPR issues can be traced back to the near-exclusive focus on CPR issues by the court, and whether this has implications for the creation of a “thick” kind of human rights accountability. It argues that, considering the nature of the Khmer Rouge's genocidal policy, it would have been within the mandate and capacity of the court to pay more attention to actions that also constituted violations of economic, social, and cultural rights (ESCR). The fact that the court did not do this and instead almost completely obscured ESCR rhetorically has triggered a similar blind spot for ESCR issues on the part of human rights NGOs, which could have otherwise played an important role in creating a culture of accountability around this category of human rights. Does this mean that violators of ESCR are more likely to escape prosecution going forward?

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.629
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.354
Teacher spread0.314 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2018
Admission routes1
Has abstractyes

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