Silence in English Cross-Cultural Interaction
Bibliographic record
Abstract
This paper aims to investigate the use of silence during interactions in the English language, cross-culturally, to determine if it is as effective as speech. It seeks to shed light on how British, American, Irish, and Canadian interlocutors use and interpret different types of silence and the functions it fulfills. The hypothesis posits that silence is universally employed by all interlocutors in all cultures and enhances the dynamics of interaction. The data consists of conversations from fifteen video-recorded English TV interviews, adopting Saville-Troike's (1985) and Nakane's (2007) models of analysis. The study concludes that silence serves the function of speech in transmitting and receiving messages, facilitating the aim of communication. Moreover, interlocutors from different cultures within the same language employ silence universally. Verbal and nonverbal communications, including silence, are inseparable, each playing a significant role. Their combined usage enhances the power of communication. Silence serves various functions beyond mere acceptance and refusal; it also encompasses face-saving and face-threatening strategies. Regarding cross-cultural differences in using silence in English, British interlocutors recorded the highest use of silence, followed by Americans, then the Irish, and lastly the Canadians. In addition, the use of silence varies depending on the context of the situation, the conversation's topic, the personalities of the interlocutors, their age, and their level of education.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".