Sorrow is physical (Depiction of non-verbal communication in Štefan Strážay’s poetry)
Bibliographic record
Abstract
The poetry of Štefan Strážay is analysed in this paper from the point of view of nonverbal communication. Nonverbal communication is a subject of growing interest to scholars in many disciplines, also in literary studies. Spanish-Canadian leading scholar Fernando Poyatos, after systematizing the conceptual, terminological and methodological apparatus of this interdisciplinary science, has applied it to the analysis of literary texts of prose fiction and theatre. This paper is inspired by the findings of Poyatos, but tries to go further, testing the applicability of this new discipline to lyrical poetry. The representation of nonverbal communication in lyrical text is usually less explicit than in the other literary genres, but thanks to the realistic nature of Strážay’s poetry it is possible to find some good examples. Due to the mainly visual nature of the poetic image, the paper focuses on the representation of kinesics –and one of its parts, proxemics– in Strážay’s poetry. Texts are selected from his representative books Wormwood, Sister, and 96 Malinovský Street. Particularly interesting are those poems in which there is a communicative interaction between two persons, the most often a man and a woman; one of them can be the lyrical subject, otherwise acting as an observer. The representation of body language in Strážay’s poems is mostly implicit, but it is important as a sign of nonverbalized feelings of the characters.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".