The Role of Syntactic Expressive Means in the English Language Economic Mass Media
Bibliographic record
Abstract
This article studies expressive syntax as a type of stylistic devices and illustrates its use in publicistic style economic oral and written media reports. The relevance of the research is that syntactic expressive means have not been thoroughly studied and analyzed in economic mass media. The work aims to identify the techniques that apply syntactic expressive means to evoke emotiveness in economic media reports. This article also addresses the recurrence of usage of expressive syntax in written and oral speech involving economic discourse. Using the method of text analysis on the bases of theoretical linguistic statements evaluating functional style, media stylistics, and stylistic devices in the English language, we determined the diverse usage of expressive syntax in both videocasting and written articles. From analyzed syntactic expressive means, we identified the frequency and common usage of such syntactic expressive means as rhetorical question and simple repetition in oral and written reports. The sample analysis indicated that a paragraph in any economic report might restrain more than one occurrence of expressive syntax; these carry a manipulative function through psychological phenomena represented via syntactic expressive means.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".