A Case Study on Characters in Pride and Prejudice: From Perspectives of Speech Act Theory and Conversational Implicature
Bibliographic record
Abstract
Speech act theory and conversational implicature, as research approaches in discourse analysis (DA), have been applied successfully to investigations in such fields as philosophy, linguistics, psychology and literature criticism. This paper aims to employ a synthesized model of these two theories to make a tentative study of the “literature language” and the characters in the literary work—Pride and Prejudice—to testify whether these research methods contribute to the readers’ understanding and appreciation of this masterpiece. The results of the study show that, to a certain extent, the image of the characters in a particular context in this literary work has been successfully demonstrated in terms of these two approaches in DA and it has been proved that “literature language” can be analyzed by means of DA theories. In addition, the study may contribute to the enlightenment of effective and creative approaches in literature as well as college movie English audio-visual-oral course teaching.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.013 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".