Verbal and bodily practices for addressing trouble associated with embodied moves in game play
Bibliographic record
Abstract
Abstract We provide a first systematic account of how interactants manage trouble that is not localizable in talk but rather in embodied conduct in table-top game play. Interventions targeting embodied problems have been termed “remedial actions” ( Lerner and Raymond 2021 ) or “remedies” ( Arminen and Auvinen 2013 : 19). Focusing on game-playing interactions in German and English, we show that remedial actions addressing a coparticipant’s prior embodied move, or the absence of a move, as troublesome can take one of three different forms: they can be embodied, verbal, or a combination thereof. We show a systematic link between the form of remedial action and the type of trouble addressed: Remedial actions that are exclusively embodied address deviations from shared playing practices, typically involving problems with game piece placement. Exclusively verbal remedial actions address violations of codified game rules such as premature moves. Remedial actions that combine verbal and embodied resources target established practices or formal rules but accomplish additional actions, for example doing teaching or reproaching. By highlighting the systematic interplay between talk and embodiment, our study contributes to a new, multimodal perspective in Interactional Linguistics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".