Bibliographic record
Abstract
Introduction The organization of repair has received sustained interest in the study of language and social interaction over the past decades. Although, until recently, research in this area has based its claims primarily on English materials, there has been a growing interest, especially in the past ten years or so, in exploring how repair operates in languages other than English. This expanding body of research includes studies of German (e.g., Egbert 1996, 1997b, 2004; Selting 1988, 1992, 1996; Uhmann 2001), Japanese (e.g., Fox et al . 1996), Korean (e.g., Kim 1999a, 2001), Thai (e.g., Moerman 1977), and Mandarin Chinese (e.g., Chui 1996; Tao et al . 1999; Wu 2006; Zhang 1998), among others. Some of these studies have focused on the mechanisms of self- and other-initiation of repair in the languages examined (e.g., Chui 1996; Kim 1999a, 2001; Moerman 1977; Zhang 1998), while others have uncovered the linkages between repair and other aspects of interactional practice, such as prosody (e.g., Selting 1996; Tao et al . 1999) and bodily conduct (e.g., Egbert 1996), and still others have explored the relation between repair and syntax from a cross-linguistic perspective (e.g., Fox et al . 1996). As part of this growing effort to understand the organization of repair across languages, this chapter investigates two repeat-formatted other-initiated repair practices in Mandarin conversation. Using the methodology of conversation analysis (CA), this study will show that the two Mandarin repair initiations under examination, like other-initiation of repair in English, serve not only to initiate repair but also as vehicles for accomplishing additional negatively valenced actions, such as displaying a stance of disbelief or nonalignment.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".