International Human Rights Fact-finding Praxis in its Living Forms: A TWAIL Perspective
Bibliographic record
Abstract
International human rights fact-finding (hereinafter "IHRFF") has been defined, rather generously, as: A method of ascertaining facts through the evaluation and compilation of various information sources ... [which] serves to illuminate the circumstances, causes, consequences and aftermath of an event from a systematic collection of facts. Understood in this way, IHRFF is not a new activity. Rather, various organizations, groups, and entities have engaged in it for a very long time. Indeed, issues relating to its ways and means, conceptual and operational problems, and best practices have occupied the attention of many practitioners, and cringed the brows of many of scholars, for a fairly long time. However, recent years have witnessed an increased deployment of IHRFF in response to alleged violations of human rights in a range of climes. This may be a possible justification for the renewed attention that it appears to receiving among academics and practitioners alike. In particular, given the increasing salience of IHRFF and the tremendous power that its practitioners can increasingly exert in both domestic and world affairs, contemporary scholarly commentators appear to be justified in renewing their quest to understand IHRFF and, if necessary, stimulate its thoughtful reform. This article is a modest attempt to contribute to the emergent process of the renewed study of that praxis.
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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 | 0.044 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.060 |
| Scholarly communication | 0.022 | 0.025 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 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".