Language matters: towards an understanding of silence and humour in medical education
Bibliographic record
Abstract
OBJECTIVES: This paper considers the state of the science regarding language matters in medical education, with particular attention to two informal language practices: silence and humour. Silence and humour pervade clinical training settings, although we rarely attend explicitly to them. METHODS: This paper considers the treatment of these topics in our field to date and introduces a selection of the scholarship on silence and humour from other fields, including philosophy, sociology, anthropology, linguistics and rhetoric. Particular attention is paid to distilling the theoretical and methodological possibilities for an elaborated research agenda around silence and humour in medical education. RESULTS: These two language practices assume a variety of forms and serve a range of social functions. Episodes of silence and humour are intimately tied to their relational and institutional contexts. Power often figures centrally, although not predictably. CONCLUSIONS: A rich theoretical and methodological basis exists on which to elaborate a research agenda around silence and humour in medical education. Such research promises to reveal more fully the contributions of silence and humour to socialisation in clinical training settings.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.064 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".