MétaCan
Menu
Back to cohort
Record W2533258796 · doi:10.1075/wlp.3.05sch

2. Interpreting at the International Criminal Tribunal for the Former Yugoslavia (ICTY)

2010· book-chapter· en· W2533258796 on OpenAlexaboutno aff
Nancy Schweda Nicholson

Bibliographic record

VenueStudies in world language problems · 2010
Typebook-chapter
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsTribunalPolitical scienceCriminologyLawSociology

Abstract

fetched live from OpenAlex

Interpreting services constitute an integral part of the day-to-day activities at the International Criminal Tribunal for the Former Yugoslavia (ICTY) in The Hague. At the opening of this paper, a focus on the differences between Civil and Common Law systems and an overview of court interpreting contribute relevant background information. An historical segment treats the creation of the ICTY and its administrative framework, thus providing the backdrop for a detailed look at the ICTY’s working languages and the use of simultaneous interpreting (SI), relay interpreting (RI) and consecutive interpreting (CI) in the courtrooms, deposition venues and Detention Unit. Inasmuch as the ICTY trials are the result of a vicious, lengthy war and ethnic cleansing activities, interpreter stress plays a significant role, both in the courtrooms and in the field. In order to facilitate the functioning of the ICTY and to orient attorneys to the unique work environment there, several training courses were held in The Hague and Montreal from 2003–2006. The ICTY’s legacy lives on as the International Criminal Court (ICC) conducts investigations in Africa and trials in The Hague.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.810
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.136
GPT teacher head0.465
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations24
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueStudies in world language problemsSame topicInterpreting and Communication in HealthcareFrench-language works237,207