Linguistics in Literary Discourse: Exploring Meaning, Construction and Alternatives Approaches
Bibliographic record
Abstract
This paper examines literary discourse patterns using the Appraisal Framework (Martin & White, 2005) and the Extended Pragma-dialectic Theory of Argumentation (van Eemeren & Grootendorst, 1984, 1992, 2004). It aims to demonstrate how these approaches offer systematic and logical methods for studying literature. By focusing on the linguistic tools through which authors and texts express, negotiate, and promote particular viewpoints, the study shows how these frameworks help reveal the ways writers convince readers of the acceptability of arguments. The Appraisal Framework provides insight into how texts reflect inter-subjective and ideological positions, while the Extended Pragma-dialectic Theory highlights how propositions justify or refute claims and counterclaims within a text. Furthermore, the paper explores the value of linguistics in literary analysis by integrating Bakhtin’s concept of dialogism. This perspective supports the idea that texts are inherently dialogic, reflecting a constant interaction of voices and ideas. By combining linguistic and literary analysis, the study argues that the Appraisal Theory and Pragma-dialectic Theory provide useful tools for understanding how texts persuade and engage readers in complex argumentation. Ultimately, it validates the relevance of these linguistic theories within literary studies.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.008 | 0.057 |
| Scholarly communication | 0.024 | 0.035 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".