A Comparative Study of Ideational Grammatical Metaphor in Health and Political Texts of English Newspapers
Bibliographic record
Abstract
Newspapers, as their name suggests, provide us with news. With the spread of education, the popularity and importance of newspapers have increased by leaps and bounds. Everybody today wants to read a newspaper. The language of mainstream newspapers is formal and special English so, there is no surprise that the grammatical metaphor identification procedure can be obviously applied to newspaper text. Systemic functional grammar constructs a grammar for the purpose of text analysis to investigate how grammar is used as a means of making meaning. Grammatical metaphor is one of the language phenomena introduced by Halliday (2004) in the framework of functional grammar. The present work focuses on the application of Hallidayian metafunctional framework in both political and health texts of English newspapers. The analysis of data was conducted through a description of English newspaper texts, based on ideational grammatical metaphor. To this end, the researcher conducted some statistics to this strand of meaning, including frequency and percentage of nominalization type of ideational grammatical metaphor in both genres. Finally, two genres of English newspapers were compared statistically to show in what respect they are significantly different or similar. The obtained results indicate that both genres of each English newspapers bear more similarities than differences in terms of using the nominalization of ideational grammatical metaphor. In other words, while indicating genre differences between English newspapers, the study proves their functional similarities in using the material process types more than other process types to convey meaning.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".