A Comparative Study of News Tweets of Tham Luang Cave Rescue by Thai, American, British, and Australian Broadcasters
Bibliographic record
Abstract
This research investigated the typical grammatical structures, topics, and address terms for the victims in the news of Tham Luang cave rescue on Twitter published by four broadcasters: Bangkok Post, CNN International, BBC World News, and ABC News. The sample was 454 news tweets posted by the broadcasters between June 25, 2018 and July 15, 2018. The instruments were a syntactic analysis table, a topic analysis table, and an address term analysis table. The results showed that (1) the most typical grammatical structure used in the news by all broadcasters, except BBC World News, was simple sentence, (2) the most frequently-addressed topics in Bangkok Post and BBC World News were settings, while those in CNN International and ABC News were victims, and (3) the most frequently-used address terms for the victims in the news by Bangkok Post and BBC World News were address terms associated with age and gender combination and those associated with occupation, while this frequency order was in reverse in the news by CNN International and ABC News. Certain statistically significant correlations were also witnessed. The findings provided insights into certain similarities and differences in typical grammatical structures, topics, and address terms in the news tweets posted by the four broadcasters, and they also reflected English-language news publication on Twitter in general.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".