THE IMPLICATION FORMATIVE POTENTIAL RECEPTIVE AND SEMANTIC POTENTIAL OF SILENCE IN MODERN LITERARY AND MEDICAL DISCOURSE
Bibliographic record
Abstract
The article aims to analyse the receptive and semantic potential of silence based on the novel “Critical Condition” (2002) by the contemporary Canadian-American physician, writer Peter Clement. The research methodology is based on the application of modern literary studies in the fields of narratology, receptive aesthetics and literary hermeneutics. The theoretical significance of the research consists in the disclosure of the narrative category of silence in the modern American literary and medical discourse. The results of the study will improve the content of training courses in the world literature and form a methodological basis for the development of special courses, theme-based seminars and academic syllabi. In the course of the study, it was found that silence within the analysed literary work symbolizes the epistemological and communicative crisis of language. The author’s intentions and receptive resource of silence in the text have been analysed. The leading role of facial expressions as a means of exteriorizing the silence effect in the “doctor — patient” communicative situation has been observed. The patient’s silence in the novel is associated with the author’s rethinking of the phenomena of illness and disability, thus stimulating the reader to embrace the active position of co-creation and receptive cooperation by filling-in the narrative “gaps” of the text. Further research is needed to study the role of the reader’s reception in constructing the silence in the “doctor — patient”communicative situation, as exemplified by the literary and medical discourse of the US prose.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.045 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".