Thematic Construction in the News Coverage of the COVID-19 Pandemic: The Pattern and Its Implication in Teaching English News-Item Text
Bibliographic record
Abstract
This present study attempts to see the pattern of thematic construction in the selected newspaper on the topic of COVID-19 pandemic in Indonesia and how it is used in teaching news item text in tertiary level. This study was conducted by using a qualitative approach through the content analysis method. The source of the data was 20 news item text from 2 popular newspapers in Indonesia. From the analysis, it was found that there are some variations in the thematic construction found in the selected newspaper on COVID-19 Pandemic in Indonesia. From its simplexity, simple and multiple Themes construction were found. Simple Theme is represented by the use of one single Topical Theme in the clauses, Meanwhile, Multiple Theme was constructed by the variation of Textual, Interpersonal, and Topical Theme. In addition, in teaching news item text, lecturers can suggest students to maintain thematic continuity within paragraphs and across the entire article since it can enhance the flow and clarity of their own writing. Then, understanding thematic patterns can guide students in producing well-structured news item text.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".