Bibliographic record
Abstract
This paper applies Speech Act Theory towards an investigation of the use and role of self-praise/positive self-assessment in the texts of three Chekhov’s plays: The Seagull, Three Sisters and The Cherry Orchard. The findings conducted with manual coding of texts for the speech acts of self-praise/positive self-assessment suggest that Chekhov employed self-praise for a number of textual and character-building functions. In particular, self-praise functions as a literary device to identify less likable characters as well as to provide a chance for more likable characters to stand up for themselves against injustice and provocation. The self-praise/positive self-assessment comes in mitigated and aggravated forms. Mitigation is mostly achieved through grammatical or phrasal means, as well as semantically through self-criticism, whereby the only form of aggravation observed in the data was other-criticism/other-derogation. Specific forms of a positive self-assessment likely unique to Chekhov’s plays are ‘linguistic brags’, i.e., contextually unjustifiable switches to French and Latin as well as ‘generational’ positive self-representation in Three Sisters. The results suggest that investigations of speeh acts in dramas could be productive for literary theory, as they shed more light on the characters development as well as the genre mastery of the playwright.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".