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
Bibliographic record
Abstract
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 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 | 0.013 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| 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".