Bibliographic record
Abstract
We are pleased to reply to the letter from Dr. Morris concerning our article entitled “Medicine as a Community of Practice: Implications for Medical Education,”1 as it encourages a wider discourse on the fundamental nature of learning within the medical community that can only serve to enhance our understanding of this important subject. Her letter continues a debate between sociocultural and constructivist perspectives of learning that often overlap and complement each other.2 Dr. Morris favors a sociocultural approach, stating that “learning-as-participation” is preferable to “learning-as-acquisition,” referring to an article by Sfard3 proposing that both approaches represent different metaphors for learning that are not mutually exclusive. We would like to make two points in reply. First, in our article, we synthesize much of the literature that views communities of practice as a social learning theory that results from a constructivist approach to education. We do not refer to any potential conflict between differing theoretical approaches and, in fact, believe that communities of practice entail both learning-as-participation and learning-as-acquisition. Second, Dr. Morris seems to believe that the two approaches are incompatible, a point with which we disagree. We can turn to Sfard3 for support. She states, “The relative advantages of each of the two metaphors make it difficult to give up either of them: Each has something to offer that the other cannot provide.” In addition, we do not believe that the sociocultural approach focuses exclusively on learning-as-participation. Learning-as-acquisition is equally important. Our article acknowledges this and emphasizes the centrality of engagement and participation to learning within communities of practice. We continue to believe that the communities of practice theory is robust enough to encompass both approaches, either as metaphors or as foundational elements of the overall theory. As Packer and Goicoechea2 observe: “Whether one attaches the label ‘learning’ to the part or to the whole, acquiring knowledge and expertise always entails participation in relationship and community and transformation both of the person and of the social world.” Richard L. Cruess, MDProfessor of surgery and core faculty member, Centre for Medical Education of McGill University, Montreal, Quebec, Canada; [email protected] Sylvia R. Cruess, MDProfessor of medicine and core faculty member, Centre for Medical Education of McGill University, Montreal, Quebec, Canada. Yvonne Steinert, PhDProfessor of family medicine and director, Centre for Medical Education of McGill University, Montreal, Quebec, Canada.
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 | 0.006 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.027 | 0.061 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".