Funktionen von Lachen in Gesprächen: eine konversationsanalytische Studie
Bibliographic record
Abstract
This thesis looks at laughter in conversations in order to find out what functions laughter can fulfil. The data that are used for the analysis are conversations involving four different families and one or two researchers. \nLaughter plays an important role in communication, as it depends on social factors. For example people laugh more when other people are present and how often we laugh also depends on who these other people are and in what situation we find ourselves. Because laughter is a social signal it is interesting to find out which role laughter can play in a conversation. \nMuch has been written about the relationship between laughter and humour or jokes but there is not much research on laughter in everyday conversations, that doesn’t only look at laughter as a response to humour. As laughter cannot only be seen as an indicator of humour, this thesis looks at the different functions of laughter in conversations and tries to give answers to questions like: Which participant initiates the laughter and how do other participants react to the laughter? What different functions does laughter have in different situations? So for the analysis the context of the laughter is very important. The methodology used for the analysis is primarily conversation analysis but the analysis also contains elements of interactional sociolinguistics. The analysis looks at the functions of laughter in five different contexts: laughter and irony, laughter and trouble-telling, laughter and teasing, laughter and disagreement and finally laughter and narratives. The analyzed examples show that laughter can fulfil different functions for each context.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| 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".