Presupposition Analysis in Written Online News Discourse CNN The Death of Liam Payne Ex One Direction Personnel
Bibliographic record
Abstract
In the digital era, people can obtain information using communication devices. The spread of information on social media is an important factor in understanding its meaning. The language used still has its own rules for communication. It has different meanings or perspectives depending on the person who interprets it. Therefore, this study aims to identify the presuppositions contained in viral news. In general, analyzing language or speech can be identified from anywhere, one of which is news. Therefore, the object of this research is the presupposition found in CNN's news style regarding THE DEATH OF LIAM PAYNE EX ONE DIRECTION PERSONNEL. This presupposition is seen in how the assumptions or inferences implied in the language utterance help provide additional meaning and estimate the context of the language, the language in discourse, conversation or others using Yule's (1996, pp.26-30) which calcifies that Presupposition has six types, they are existential, factive, non-factive, lexical, structural, and counterfactual. However, there are two additional additions, namely adverbial and relative additions. Considering these factors, this study used a qualitative descriptive design. The reason for choosing a qualitative descriptive design is that "the aim of qualitative descriptive research is a comprehensive summary, in everyday terms, of specific events experienced by individuals or groups of individuals" (Lambert & Lambert, 2012). From the results of the analysis and identification, 12 presuppositions were found in the written online news text; adverbial 3 (21.4%), factive 2 (14%), lexical 2 (14%), non-factive 1 (7%), relative 1 (7%), existential 2 (14%), and counter 1 (7%). The most dominant type of presupposition found in the CNN news transcript is factive presupposition. Factive presuppositions are the most common type of presupposition found in news texts they provide accurate information and contain facts.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".