Having our cake and eating it too: seeking the best of both worlds in expertise research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
CONTEXT: Education researchers in a variety of disciplines have attempted to use their understanding of expert processes to inform learning across the continuum from school learning to lifelong learning. In medical education, this has led to models of expertise that aim to understand accurate and efficient clinical reasoning. More recently, researchers outside medicine have begun to develop models of 'adaptive expertise'. As these additional constructions of expertise are introduced into health professions education, there is considerable potential to enhance research in medical expertise by providing opportunities for us to identify our implicit assumptions and reflect on the ways in which our theoretical lenses bias our perceptions of what it means to be an expert. METHODS: Firstly, we critically examine these two broad categories of research on expertise and their underlying assumptions and implications. Our exploration is organised around four main questions: (i) How is expertise defined? (ii) How does it develop? (iii) What is investigated? (iv) Based on what is known, what does an expert look like? Secondly, we discuss some implications and topics of future inquiry for research programmes informed by an inclusive understanding of expert practice and development. CONCLUSIONS: In articulating two paradigms of expertise, our goal is to explore the research questions, methods and findings that underpin them and to make explicit the resulting emphases on specific aspects of expert performance. Our resulting collaborative understanding of expertise yields a richer, more complex and ultimately more accurate view of expert performance, with important implications for future research in medical education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it