Bibliographic record
Abstract
This edited volume documents the state of the art in research on translation policies in legal and institutional settings. Offering case studies of past and present translation policies from several parts of the world, it allows for a compelling comparison of attitudes towards translation in varying contexts. \n \nThe book highlights the virtues of integrating different types of expertise in the study of translation policy: theoretical and applied; historical and modern; legal, institutional and political. It effectively illustrates how a multidisciplinary perspective furthers our understanding of translation policies and unveils their intrinsic link with topics such as multilingualism, linguistic justice, minority rights, and citizenship. In this way, each contribution sheds new light on the role of translation in the everyday interaction between governments and multilingual populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.008 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.980 | 0.967 |
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; both teacher heads agree on what is shown here.
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".