Case Study: The International Criminal Tribunal for the Former Yugoslavia’s Court Transcripts in Bosnian/Croatian/Serbian—Part 1: Needs, Feasibility, and Output Assessment
Bibliographic record
Abstract
International Criminal Tribunal for the Former Yugoslavia (ICTY) remains the most important organization for the past, the present, and the future of the former Yugoslavia. Faced with a country that always lived under totalitarian regimes with very little insight into actions of the groups and individuals who reaped unthinkable havoc on each other at the end of the twentieth century, the ICTY set undisputable historical record about events that took place during the 1991–1999 wars and put the country on an excellent track towards transformation for the better. But even 28 years since the establishment of the ICTY, the former Yugoslavia remains the hotbed of nationalism, ethnic divisions, genocide denial, and genocide justification. Court transcripts belong to the category of the permanent court record. The ICTY court transcripts have only been made in English and French, but not in Bosnian/Croatian/Serbian (B/C/S), the languages of the former Yugoslavia. This paper is going to examine the needs for the ICTY court transcripts in the B/C/S, could they have been made in the B/C/S from the very beginning of the institution and whether the existing ICTY court transcripts in the B/C/S are up to par for any of its audiences.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".