Philosophy of Science Series: Harnessing the Multidisciplinary Edge Effect by Exploring Paradigms, Ontologies, Epistemologies, Axiologies, and Methodologies
Bibliographic record
Abstract
Health professions education (HPE) researchers come from many different academic traditions, from psychology to engineering to rhetoric. Trained in these traditions, HPE researchers engage in science and the building of new knowledge from different paradigmatic orientations. Collaborating across these traditions is particularly generative, a phenomenon the authors call the multidisciplinary edge effect. However, to harness this productivity, scholars need to understand their own paradigms and those of others so that collaboration can flourish. This Invited Commentary introduces the Philosophy of Science series-a collection of articles that introduce readers to 7 different paradigms that are frequently used in HPE research or that the authors suggest will be increasingly common in future studies. Each article in the collection presents a concise and accessible description of the main principles of a paradigm so that researchers can quickly grasp how these traditions differ from each other. In this introductory article, the authors define and illustrate key terms that are essential to understanding these traditions (i.e., paradigm, ontology, epistemology, methodology, and axiology) and explain the structure that each article in this series follows.
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.
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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".