Disruption in the space–time continuum: why digital ethnography matters
Bibliographic record
Abstract
There is increasing interest in the use of ethnography as a qualitative research approach to explore, in depth, issues of culture in health professions education (HPE). Our specific focus in this article is incorporating the digital into ethnography. Digital technologies are pervasively and increasingly shaping the way we interact, behave, think, and communicate as health professions educators and learners. Understanding the contemporary culture(s) of HPE thus means paying attention to what goes on in digital spaces. In this paper, we critically consider some of the potential issues when the field of ethnography exists outside the space time continuum, including the need to engage with theory in research about technology and digital spaces in HPE. After a very brief review of the few HPE studies that have used digital ethnography, we scrutinize what can be gained when ethnography encompasses the digital world, particularly in relation to untangling sociomaterial aspects of HPE. We chart the shifts inherent in conducting ethnographic research within the digital landscape, specifically those related to research field, the role of the researcher and ethical issues. We then use two examples to illustrate possible HPE research questions and potential strategies for using digital ethnography to answer those questions: using digital tools in the conduct of an ethnographic study and how to conduct an ethnography of a digital space. We conclude that acknowledging the pervasiveness of technologies in the design, delivery and experiences of HPE opens up new research questions which can be addressed by embracing the digital in ethnography.
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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 | 0.209 | 0.286 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.012 | 0.055 |
| Scholarly communication | 0.018 | 0.040 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.008 | 0.007 |
| 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".