Sociomateriality
Bibliographic record
Abstract
Distributed medical education (DME) is a type of distance learning in which students participate in medical education from diverse geographic locations using Web conferencing, videoconferencing, e-learning, and similar tools. DME is becoming increasingly widespread in North America and around the world.Although relatively new to medical education, distance learning has a long history in the broader field of education and a related body of literature that speaks to the importance of engaging in rigorous and theoretically informed studies of distance learning. The existing DME literature is helpful, but it has been largely descriptive and lacks a critical "lens"-that is, a theoretical perspective from which to rigorously conceptualize and interrogate DME's social (relationships, people) and material (technologies, tools) aspects.The authors describe DME and theories about distance learning and show that such theories focus on social, pedagogical, and cognitive considerations without adequately taking into account material factors. They address this gap by proposing sociomateriality as a theoretical framework allowing researchers and educators to study DME and (1) understand and consider previously obscured actors, infrastructure, and other factors that, on the surface, seem unrelated and even unimportant; (2) see clearly how the social and material components of learning are intertwined in fluid, messy, and often uncertain ways; and (3) perhaps think differently, even in ways that disrupt traditional approaches, as they explore DME. The authors conclude that DME brings with it substantial investments of social and material resources, and therefore needs careful study, using approaches that embrace its complexity.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.007 |
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".