Development of the Engineering Technology Word List for Vocational Schools in Malaysia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The increasing demand for specialised instruction or lexis for Non-Native English Speakers (NNES) in various disciplines has brought about extensive research of specialized vocabulary in academic texts which help learners to make acquainted with their discourse communities. The word list which consists of the most essential words or known as “building blocks” in the specialized field is regarded as one of the most significant prerequisites in terms of curriculum development. This research emphasizes on the most frequently used engineering academic vocabulary in the form of an engineering technology word list developed using locally written Malaysian engineering technology textbooks for vocational programmes in upper secondary education. The frequently used engineering technology words are selected from the vocational-programme engineering corpus (VPEC) to enhance English for Engineering Purposes (EEP) learning. A word list named Engineering Technology Word List (ETWL) is developed and it is a valuable resource to English for Engineering Purposes (EEP) in Malaysia. The introduction of this word list can be a source of reference where key vocabulary can be accessed for curriculum development in vocational programmes. Besides that, in order for the publishers and EEP textbook writers to further advance the arrangement of vocabulary in developing EEP material, the ETWL should be the key reference.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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 it