Bibliographic record
Abstract
Introduction In recent years, one major focus of conversation analysis (CA) has been to investigate how knowledge is distributed and negotiated among participants in interaction. Research has demonstrated that for conversationalists what each of them knows and how they know it are accountable matters (Heritage 1984a, 1998; Drew 1991; Schegloff 1996b; Roth 2002; Heritage and Raymond 2005; Raymond and Heritage 2006; Stivers 2005; Clift 2006a, 2006b; Golato and Fagyal 2008). As is the case for most CA research, the majority of these studies have been based on the analysis of English data. However, as Schegloff (2006) argues, human beings as a species face the same problems and issues in everyday life independently of language or culture. As “the primary, fundamental embodiment of sociality” (Schegloff 2006: 70), interaction is designed to address these problems and issues, though the way in which this gets done varies across languages or cultures (cf. Schegloff 2002a). It thus stands to reason that practices parallel or analogous to those described for English exist in other languages. Once identified, such practices may provide a basis for cross-linguistic comparison – allowing us to see the way in which the local resources of a particular language are mobilized to solve generic problems of interaction. In this chapter, I explore how Danish speakers orient to knowledge in interaction. Specifically, I identify two Danish practices, through which a recipient can index that a question is inapposite, because it inquires into something that should already be known to the questioner, and compare these with one previously described for English.
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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 | 0.021 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.010 | 0.028 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".