Bibliographic record
Abstract
Altered States: Translation and Minority Languages — The linguistic complexity of Europe is often ignored in political accounts of its translation practice. In particular, the historical experience and contemporary fate of European minority languages are overlooked in assessing the translation strategies available to speakers of minority languages. The problem partly results from a failure to think creatively about definitions of minority languages in a translation context. This context includes the dimension of new technologies which may lead to a new reclassification of languages in Europe and elsewhere. The role of translation in the case of one European minority language, Irish Gaelic, is considered in terms of the dilemmas faced by lesser used languages. Translation is both welcomed and feared. The options available to translators in minority languages differ crucially from those on offer to translators in majority languages. These differences need to be reflected in the theoretical discourse on translation in minority languages but this is not often the case. Furthermore, translation studies as a discipline rarely reflects on its own majority language bias, embedded in the structures of the disciplinary dissemination of knowledge. Minority languages are not only essential to a diversity that sustains the fragile ecosystem of human culture but they also raise questions that lie at the heart of translation studies as an area of intellectual inquiry.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.043 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".