Nonverbal Communication across Disciplines: Volume 1: Culture, sensory interaction, speech, conversation
Bibliographic record
Abstract
In a progressive and systematic approach to communication, and always through an interdisciplinary and cross-cultural perspective, this first volume presents culture as an intricate grid of sensible and intelligible sign systems in space and time, identifying the semiotic and interactive problems inherent in intercultural and subcultural communication according to verbal-nonverbal cultural fluency. The author lays out fascinating complexity of our direct and synesthesial sensory perception of people and artifactual and environmental elements; and its audible and visual manifestations through our ‘speaking face’, to then acknowledge the triple reality of discourse as ‘verbal language-paralanguage-kinesics’, which is applied through two realistic models: (a)for a verbal-nonverbal comprehensive transcription of interactive speech, and (b)for the implementation of nonverbal communication in foreign-language teaching. The author presents his exhaustive model of ‘nonverbal categories’ for a detailed analysis of normal or pathological behaviors in any interactive or noninteractive manifestation; and, based on all the previous material, his equally exhaustive structural model for the study of conversational encounters, which suggests many applications in different fields, such as the intercultural and multisystem communication situation developed in simultaneous or consecutive interpretating. 956 literary quotations from 103 authors and 194 works illustrate all the points discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".