Ethnographies across virtual and physical spaces: a reflexive commentary on a live Canadian/UK ethnography of distributed medical education
Bibliographic record
Abstract
This article draws on an ongoing ethnography of distributed medical education (DME) provision in Canada in order to explore the methodological choices of the researchers as well as the wider pluralisation of ethnographic frameworks that is reflected within current research literature. The article begins with a consideration of the technologically mediated ways in which the researchers do their work, a way of work that is paralleled within the DME curriculum that forms the focus of the ethnography. The article goes on to problematise relationships amongst the researchers and between the researchers and the field of research, and to consider the ways in which methodological choices are mediated. In so doing, the article proposes an acceptance of methodological pluralism that is tempered by the need to acknowledge the sometimes-slight differences that distinguish ethnographic paradigms.
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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.053 | 0.071 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".