Bibliographic record
Abstract
An understanding of the diversity of perspectives within the research paradigms of health professions education (HPE) is essential for rigorous research design and more purposeful engagement with the contributions of others. In this article, the authors explicitly discuss the underlying assumptions, notions of good scholarship, and shortcomings of the postpositivism research paradigm. While postpositivism is likely one of the more familiar paradigms within HPE research, it is rarely formally or explicitly described. Drawing on key literature and contemporary examples, the authors describe the ontology, epistemology, methodologies, axiology, signs of rigor, and common critiques of postpositivism. A case study provides the focus for a practical illustration of how a postpositivist approach to education research could be applied. Suggestions for further reading are provided for those who are keen to delve deeper into the history and key tenants of the postpositivist stance.
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 | 0.274 | 0.283 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.021 | 0.188 |
| Scholarly communication | 0.030 | 0.033 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.007 | 0.018 |
| 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".