Bibliographic record
Abstract
Abstract Cultural genocide remains a recurrent topic, appearing not only in the form of wide-ranging claims about the commission of cultural genocide in diverse contexts but also in the legal sphere, with recent discussions before the International Criminal Tribunal for the Former Yugoslavia and in the drafting of the UN Declaration on the Rights of Indigenous Peoples. These discussions have however displayed the lack of a uniform understanding of the concept of cultural genocide and, consequently, of the role that international law is expected to fulfil in this regard. This book therefore intends to present an exhaustive review of how international law has been approaching the core idea underlying the concept of cultural genocide, i.e. the intentional destruction of group cultures, and how this framework can be strengthened and fostered. It therefore traces developments, from the early conceptualization of cultural genocide to the contemporary question of its reparation. Through this journey, it discusses the evolution of various branches of international law in relation to both cultural protection and cultural destruction, in light of a number of legal cases in which either the concept of cultural genocide or the idea of cultural destruction has been discussed. Such cases include the destruction of cultural and religious heritage in Bosnia and Herzegovina, the forced removals of Aboriginal children in Australia and Canada, and the case law of the Inter-American Court of Human Rights in relation to Indigenous and tribal groups’ cultural destruction.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.070 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".