Cognitive and Intersemiotic Model of the Visual and Verbal Modes in a Screen Adaptation to Literary Texts
Bibliographic record
Abstract
The aim of the study is to examine screen adaptations from the perspective of cognitive and intersemiotic models of the visual and verbal modes. The purpose of the study is to express the specificity of a screen text which is defined as a combination of three media: speech, image, and music. The scope is to demonstrate the general framework of an intersemiotic translation from a new point of view – like a transliteration. The method of the research refers to semiotic and stylistic analyzes – methods of transformation from one sign system into another from prose works with regard to their cognitive as well as narrative and stylistic features (Zhong, Chen, & Xuan, 2021). Thus, the study analyses such specific relations between the verbal and visual modes in film adaptations of prose literature as a more detailed description of event episodes, events’ temporal structure, presentation of author’s thoughts and characters’ thoughts; their mental activity formulated indirect speech and inner speech that is shown only by the actor’s intonation. The results of the study made possible to show the types of inner speech in their adaptations: author’s thoughts, characters’ thoughts which are presented only by the verbal mode, and visual modes’ inner speeches that combine the modes of character’s voice and image. One can conclude, that taking into account intersemiotic relations between the visual and verbal spaces, it is possible to explain, for instance, how the words of characters are replaced by their facial expressions, gestures, or intonations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".