Bibliographic record
Abstract
Introduction This chapter describes a language-specific solution to a generic and likely universal interactional issue – how to show that the current utterance is occasioned by and should be understood by reference to something other than the immediately preceding talk. While the default way of connecting an utterance to a prior one is by placing it immediately after the targeted turn (Sacks 1987, 1995; Sacks et al . 1974), on occasion interlocutors find themselves in need of showing that their current turn is noncontiguous with what came just before. One common interactional task then is to connect the current utterance to some early talk, what Sacks refers to as “skip-connecting” (Sacks 1995: II, 349–351, 356–357). Using the methodology of conversation analysis (CA) to examine Russian language conversations, this chapter focuses on one linguistic resource interlocutors can use to manage this interactional problem: the Russian discourse particle -to . An investigation of over sixty hours of recorded interactions between native Russian speakers demonstrates that this particle is deployed in order to index the delayed placement of the action implemented by the current turn-at-talk. The particle is typically placed after a word repeat that helps locate the target of the displaced action in prior talk. The chapter starts with a cross-linguistic overview of several currently documented solutions to the problem of contiguity breaks in talk-in-interaction. Turning attention to Russian, I examine some contexts in which the particle -to is used, including delayed clarification requests and resumptions of previously closed or abandoned courses of action.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".