Small talk, high stakes: Interactional disattentiveness in the context of prosocial doctor-patient interaction
Bibliographic record
Abstract
ABSTRACT The literature on “small talk” has not described the way in which this talk, even as it “oils the social wheels of work talk” (Holmes 2000), enables disattending to the instrumental tasks in which one or both participants may be engaged. Small talk in simultaneity can disattend to the movements, bodily invasions, and recording activities functional for the instrumental tasks of medicine. Small talk in sequence occurs in sensitive sequential environments. Surgeons may use small talk to focus away from psychosocial or other concerns of patients that may focus off the central complaint or treatment recommendation related to that complaint. Patients may use small talk to disattend to physician recommendations regarding disfavored therapies (such as exercise). Overall, small talk often may be used to ignore, mask, or efface certain kinds of agonistic relations in which doctor and patient are otherwise engaged. We explore implications of this research for the conversation analytic literature on doctor–patient interaction and the broader sociolinguistic literature on small talk.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.028 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".