Using Talanoa to develop bilingual word lists of technical vocabulary in the trades
Bibliographic record
Abstract
It is important for anyone entering a profession to learn their profession’s specialised language. This is also true of those learning trades such as automotive technology or plumbing. Knowledge of specialised language allows trades professionals to speak to other professionals and read technical material. Although this technical language is new to all students, learning it is harder for students learning in a second language. In this article we provide support for this learning for students from the Pacific Island nation of Tonga, who are studying a trade in English either in Tonga or abroad. In prior work we developed technical word lists in four trades, identifying the technical vocabulary in a 1.6 million-word corpus of course material. In this article, we extend that research by developing bilingual English-Tongan word lists, using culturally appropriate Talanoa methodology to draw on the specialist knowledge of Tongan-speaking trades’ professionals. Translation revealed that numerous technical words do not have a direct translation in Tongan, particularly infrequent English words. It also revealed words with a clear Tongan equivalent, and Tonganised English words. The bilingual word lists will benefit Tongan trades’ trainees in Tonga and those who are migrants to English countries.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".