Barriers to cross-disciplinary knowledge flow: The case of medical education 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
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.
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 | 0.015 | 0.161 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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 it