Cognitive metaphors of expertise and knowledge: prospects and limitations for medical education
Bibliographic record
Abstract
CONTEXT: Many approaches to the study of expertise in medical education have their roots most strongly established in the traditional cognitive psychology literature. As such, they take a common approach to the construction of expertise and frame their questions in a common way. This paper reflects on a few of the paradigmatic assumptions that have 'come along for the ride' with the traditional cognitive approach, and explores what might have been left out as a consequence. METHODS: We examine the operational definition of 'expert' as it has evolved using the traditional cognitive paradigm and we explore some alternative definitions and constructions of expert performance that have arisen in parallel education research paradigms. We address 3 inter-related aspects of expertise as manifested in the traditional cognitive approach: the construction of the expert as a (routine) diagnostician; the construction of the developmental process as the (automatic and un-reflective) accrual of resources through experience, and the construction of accrued knowledge as a relatively static resource that is subsequently used and built upon with further experience. CONCLUSIONS: We hope that, by highlighting these issues, we may begin to marry the strengths of the traditional cognitive paradigm with the strengths of these other paradigms and expand the scope of cognitive research in medical expertise.
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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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.042 |
| Scholarly communication | 0.010 | 0.031 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".