Bibliographic record
Abstract
Recent studies illustrate cases of turn continuations that are not necessarily criterially dependent on clausal syntax (Couper-Kuhlen & Ono, 2007 Couper-Kuhlen, E. and Ono, T. 2007. Incrementing in conversation: A comparison of methods in English, German and Japanese [Special issue]. Pragmatics, 17(4): 513–552. [Google Scholar]; Ford, Fox, & Thompson, 2002 Ford, C. E., Fox, B. A. and Thompson, S. A. 2002. “Constituency and the grammar of turn increments.”. In The language of turn and sequence Edited by: Ford, C. E., Fox, B. A. and Thompson, S. A. 14–38. Oxford, , UK: Oxford University Press.. [Google Scholar]), advancing a more multidimensional construal of turn expansions, in general, which, as Auer (2007) Auer, P. 2007. Why are increments such elusive objects? An afterthought [Special issue]. Pragmatics, 17(4): 647–658. [Google Scholar] put it, “is not a syntactic issue alone” (p. 651). This study further develops such a possibility and looks at a class of examples in Japanese conversation whereby a series of syntactically disjunct clausal units are both retrospectively oriented toward and pragmatically, semantically, and prosodically coherent with preceding material, often acting functionally as continuations. Some implications for the role of syntax in theory regarding turn-constructional unit continuations, specifically, and turn-taking, in general, are also discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".