A Pragma-Stylistic Study of Some Selected Fantasy Novels
Bibliographic record
Abstract
The present study tries to investigate the field of pragma-stylistics in literary text in general, and fantasy novels in particular. Therefore, it tries to attest how pragmatic theories are employed stylistically to achieve the aims of the literary writers and to reflect their perceptions. The present study tries to achieve the following aims: Specifying the most dominant categories of speech acts used by characters and the narrator in the two novels to achieve some stylistic effects. Showing how the non-observance of the maxim yield effects on the two levels of interaction, and presenting the most dominant non-observed maxim in the selected fantasy novels. Clarifying the way the difference in the writing period of each novel can affect the readers pragmatically and stylistically through finding out what is the most dominant figure of speech at the character-character level as well as the narrator-reader level of interaction and whether they are employed stylistically or not. The present study is limited to two theories of pragmatics: speech act theory and Grice maxims. And the data of the analysis is limited to the children’s fantasy novels, one is written in the 1950’s, the other in 2000’s. After analysing the data, it is concluded that the most dominant SA that is used is the representative SA. Flouting the maxims yield effects on the two levels of interaction, generating conversational implicature. Finally, the difference in the writing period of each novel affect the readers pragmatically and stylistically through finding out the most dominant figure of speech, which is irony, at the character-character and the narrator-reader level of interaction.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".