Non-Verbal Communication in the Modern World
Bibliographic record
Abstract
This article is devoted to the subject of non-verbal communication in English-speaking countries. In the first part of this article we analyze the theoretical issues as Communication Theories in Linguistics and Psycholinguistics, as well as Perception Theory and Non-verbal and Verbal communication in general. Comparative analysis of specify for non-verbal communication with the example of English-speaking countries such as the USA, the UK, Australia, Canada, India, New Zealand is shown in the second part . The main conclusion is that in spite of the fact that these countries are English speaking they have both similar and different non-verbal communication signs and all these differences depend on various cultural contexts, mentality as well as the perception of non-verbal signs. The main idea of this article can be valuable for the world of Psycholinguistics and modern communication because it shows all the important cues of non-verbal communication which every time helps in communication act. DOI: 10.5901/mjss.2016.v7n4p553
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".