Translation as a Provider of Models of Sociological Discourse in Nusantara
Bibliographic record
Abstract
Translation as a Provider of Models of Sociological Discourse in Nusantara — The social sciences have seen rapid growth both as academic subjects and as instruments of national development in the Malay language nations of SE Asia: Brunei Darussalam, Indonesia and Malaysia. The particular nature of social science terminology and discourse has presented special problems for translators of social science texts, who have been at the frontiers of language creation as national language texts have been increasingly used at all levels of education in Indonesia and Malaysia. In Indonesia, where higher education had been Indonesian-medium after independence, the first social science texts to be translated were from Dutch, but, following the departure of the Dutch, extensive American support to social science education by the USA from the 1960s led to a new wave of texts translated from English. In Malaysia the decision to introduce Malay-medium higher education created a need for translations of key texts from English. In Brunei Darussalam, while higher education is English-medium, Malay-medium university students have found it necessary to translate English social science material to succeed in their learning. While the three countries have an agreement to standardise terminology and discourse, social science language has to some extent diverged. Meanwhile a serious shortage of qualified translators has hampered the production of adequate and sufficient translations. This paper discusses (1) the issues of "transparency" and "invisibility" in providing Indonesian and Malay target texts and (2) the feasibility of "domesticating" concepts and methodologies and providing recipient language texts which are usable and developmental.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".