Towards Specialized Translator Studies in a Minority-Language Context: The Multiple Lives and Times of Uya Pawan, Hero of Language Development
Bibliographic record
Abstract
Although scholars had been interested in translators’ agency since the 1990s, Andrew Chesterman introduced new terminology when he proposed an “agent model” for “Translator Studies” in 2009. While subsequent studies have shed light on the agency of literary translators, specialized translators have remained in the shadows, including in minority translation studies. This article is a case study of Uya Pawan, a specialized translator who translates ethnobotanical descriptions between Chinese, the majority language of Taiwan, and Sediq, a critically endangered Indigenous minority language spoken in central Formosa. In settler states around the world, in a race against time, heroic minority-language translators like Uya are translating specialized texts to develop ancestral tongues. As a hero of language development through specialized translation, Uya is an agent par excellence. But an agent model for translator studies must be complemented by a structural interpretation of why, what, and how a translator is translating, and with what effect. While Reine Meylaerts conducted such an analysis within a neo-Bourdieusian framework, this article adopts the framework of Anthony Giddens, whose structuration theory better accommodates a heroic view of linguistic history. There are different ways of being a linguistic hero; Uya’s leads not to purism, but rather, surprisingly, to compromise with the majority language.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.041 | 0.032 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".