Bibliographic record
Abstract
Dr Barrie Sander’s monograph is an original and important contribution to scholarship on the historiography and methodology of international trials. This profound study comes at a time of growing fatigue with international criminal justice and its institutions in certain previously supportive quarters, alongside dampened enthusiasm, even cynicism, about their efficacy as responses to situations of mass atrocity. Dr Sander meticulously examines not just what he calls ‘the struggle for historical justice’ (discursive contests between different actors vying for judicial acknowledgement of their preferred interpretations of the past) but proves how historical narratives are construed and communicated at international criminal courts and tribunals through individuals at particular times and their actions/inactions at certain stages of legal proceedings and afterwards. He has been able to do this through application of a tri-partite conceptual framework: focusing on the actors involved in struggles for historical justice, the questions around which such struggles tend to be structured, and the practices through which such struggles are conducted. He takes us through the historical function of international courts and processes of international justice, exploring in particular how such courts construct historical narratives about both the culpability of the accused and the broader mass atrocity contexts in which they are alleged to have participated.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.312 | 0.198 |
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".