Syllabus Development on Writing English News Stories for Kasetsart University Students, Thailand
Bibliographic record
Abstract
An English syllabus was developed on writing English news stories for the English for Journalism course in the second semester of the 2014 academic year at Kasetsart University, Kamphaeng Saen campus, Thailand. The study focused on the development of material for writing hard-news and feature stories. The sample consisted of 154 students who had majored in English. The development and analysis of the syllabus for the course used a book titled English News: Reading and Writing developed by Peking University Press (2008). The analysis implemented current news stories in the course for their social and cultural contexts. The findings indicated that the designed curriculum worked well to some degree but that it was limited by the students’ lack of familiarity with writing news stories. We suggest various actions: 1) to further develop students’ writing ability, the lecturer should integrate more comprehensible inputs and material apart from those in the book; and 2) the news input material could be sourced from current news stories around the world with particular attention to Appraisal Framework (Martin & White, 2005) and the learning cycle proposed by the Sydney genre-based school (Martin & Rose, 1994), which are the main approaches under systemic functional linguistics, focusing on metafunctions (field, mode, tenor), the context of situation and the context of culture.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".