Inter-State Communications before UN Human Rights Treaty Bodies: Testing the Waters for Collective Communications
Bibliographic record
Abstract
Abstract The awakening of inter-state communications with the first ever three cases before the Committee on the Elimination of Racial Discrimination in 2018 has inspired new avenues of research about their potential and shortfalls. This article opens a new line of exploration, considering the mechanism’s potential as an avenue for collective action at a time when many States are responding to violations of international law, even when not directly affected by those violations. Those responses have included massive third-party interventions (Ukraine v Russia), and the initiation of proceedings before the icj by States not directly injured (The Gambia v Myanmar, Canada and the Netherlands v Syrian Arab Republic, South Africa v Israel). This article argues that enabling collective inter-state communications before UN treaty bodies could strengthen the mechanism as an avenue for treaty compliance and the protection of human rights, and for amplifying sovereign voices as part of peaceful dispute settlement processes.
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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 | 0.101 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.016 | 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".