Topic Maintenance and Topic Transition in a Couple’s Dinnertime Conversation
Bibliographic record
Abstract
This paper investigates the patterns of topic maintenance and topic transition used to create conversation and their accompanying structural features speakers employ to signal these two conversational phenomena. The data are a 21-minute dyadic dinnertime conversation between a boyfriend and girlfriend; they are both native speakers of American English, in their late twenties. The two significant findings of the study are as follows. First, the data reveal that the speakers used three major techniques, namely minimal responses, substitutions, and deletions, to maintain the same topic of the conversation (Goffman, 1983; Radford & Tarplee, 2000; Abu Akel, 2002; Sukrutrit, 2010; Jeon, 2012). Second, in the analysis of topic transition, the data show that the speakers resorted to different types of topic transitions (i.e., collaborative, unilateral, linked, minimally linked, and sudden) to end an ongoing topic and start a new one (West & Garcia, 1988; Ainsworth-Vaughn, 1992; Okamoto & Smith-Lovin, 2001; Sukrutrit, 2010; Jeon, 2012).
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".