Developing professional responsibility in medicine : A sociomaterial curriculum
Bibliographic record
Abstract
This book presents leading-edge perspectives and methodologies to address emerging issues o concern for professional learning in contemporary society. The conditions for professional practice and learning are changein dramatically in the wake of globalisation, new modes of knowledge production, new regulatory regiemes and increased economic-political pressures. A number of chllenges for professional learning emerge:more practitioners becoming involved in interprofessional collaborationdevlopments in new technologies and virtual workworldsemergence of transnational knowedge cultures and interrelated circuits of knowledge.The space and time relations in which professional practice and learning are mebedded are becoming more complext, as are the epistemic underpinnings om professional work. Together these shifts bring about intersections of professional knowledge and responsibilities that call for new conceptions of professional knowing. Exploring what the authors call sociomaterial perspectives on professional learning, they argue that theories that trace not just the social but also the material aspects of practice - tools, texts, bodies, devices - are useful for coming to terms with the challenges above. Reconceptualising professional learning develops these issues through specific contemporary cases focused on one of the book's three main themes: (1) professionals' knowing in practice, (2) professionals' work arrangements and technologies and (3) professional responsibility. Each chapter draws on innovative theory to highlight the sociomaterial webs through which professional learning may be reconceptualised. Authors are based in Australia, Canada, italy, Norway, Sweden and the USA as well as the UTK and their cases are based in a range of professional settings including medicine, teaching, nursing engineering, social services, the creative industries and more. By presenting detailed accounts of these themes from a sociomaterial perspective, the book raises new questions and opens new methodological approaches. These can help make more visible what is often invisible in today's messy dynamics of professional learning, and pont to new ways of configuring educational support and policy for professionals.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".