Peculiarities of Using Stylistic Means in American Artistic Discourse
Bibliographic record
Abstract
The article analyzes the use of stylistic means in contemporary American novels about the challenges of postmodern society, such as terrorist attacks, loneliness in digital reality, and digital technologies. Descriptive, continuous sampling, dictionary definitions, and contextual and component analysis methods were used to analyze the language material from novels by Douglas Coupland and Don DeLillo. The analyzed stylistic units were diverse and characterized by an emotional and evaluative component, with negative assessments being the most common. The units were divided into thematic groups, such as drugs, money, human behavior, and success/failure. The place and role of stylistic units in the novels were related to their content and stylistic features, with colloquial lexical items playing a significant role in creating a special atmosphere. The study also identified prospects for analyzing current trends in the development of English spoken language based on the works of contemporary American authors. The article concludes that excerpts from the analyzed novels can be used in the study of professional terminology and in seminars on the course "Features of literary translation" to help students compare online terminology and Internet jargon in English and Ukrainian. The Internet as an object of fiction and the use of these works of fiction in teaching English deserve further study.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".