Barriers to cross-disciplinary knowledge flow: The case of medical education research
Bibliographic record
Abstract
INTRODUCTION: The medical education research field operates at the crossroads of two distinct academic worlds: higher education and medicine. As such, this field provides a unique opportunity to explore new forms of cross-disciplinary knowledge exchange. METHODS: Cross-disciplinary knowledge flow in medical education research was examined by looking at citation patterns in the five journals with the highest impact factor in 2017. To grasp the specificities of the knowledge flow in medical education, the field of higher education was used as a comparator. In total, 2031 citations from 64 medical education and 41 higher education articles published in 2017 were examined. RESULTS: Medical education researchers draw on a narrower range of knowledge communities than their peers in higher education. Medical education researchers predominantly cite articles published in health and medical education journals (80% of all citations), and to a lesser extent, articles published in education and social science journals. In higher education, while the largest share of the cited literature is internal to the domain (36%), researchers cite literature from across the social science spectrum. Findings suggest that higher education scholars engage in conversations with academics from a broader range of communities and perspectives than their medical education colleagues. DISCUSSION: Using Pierre Bourdieu's concepts of doxa and field, it is argued that the variety of epistemic cultures entering the higher education research space contributes to its interdisciplinary nature. Conversely, the existence of a relatively homogeneous epistemic culture in medicine potentially impedes cross-disciplinary knowledge exchange.
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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 | 0.148 | 0.352 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.018 | 0.020 |
| Scholarly communication | 0.029 | 0.031 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".