Backchannel Responses and Enjoyment of the Conversation: The More Does Not Necessarily Mean the Better
Bibliographic record
Abstract
This study examined the types of backchannel response as well as its relationship with speaker presentation, listener recall, and participants’ perceived enjoyment of the conversation in an intercultural setting. Participants were 40 Anglo-Canadians and 40 Mainland Chinese, forming 40 same-gender dyads and performing two dialogues. All interactions were video-taped and micro-analyzed. Noteworthy findings include the following: 1) The Chinese participants in the role of listeners made significantly more backchannel responses than their Canadian counterparts in performing Task 2. 2) “Nod” and “okay” had the highest frequencies in both cultural groups. However, the Canadians used “repeat” more frequently than Chinese and the Chinese used “uhm” and “yeah” more than the Canadians. Participants in both groups “switched codes” when making backchannel responses, providing support for communication accommodation theory. 3) A significant negative correlation was found between the frequency of backchannel responses and participants’ self-reported level of enjoyment of the conversation, raising the critical issue of how to balance the appropriate amount of backchannel response in intercultural communication.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".