Questioning clerkship: applying Popper’s evolutionary analysis of learning to medical student training
Bibliographic record
Abstract
Purpose Through a series of critical discussions on Karl Popper’s evolutionary analysis of learning and the non-authoritarian values it promotes, the purpose of this paper is to advocate a Popperian approach for building medical student knowledge. Specifically, it challenges positivist assumptions that permeate the design and management of many educational institutions, including teaching hospitals, by considering what does and does not happen when learning takes place. Design/methodology/approach To illustrate how Popper’s approach differs from such a conception of learning, the paper examines the exchange between a preceptor (Sam) and a medical student (Lisa). The following exchange is based on the observations during a team meeting in a Canadian teaching hospital. The authors sent the transcript of the observation to Lisa for her comments. The statements in italics represent Lisa’s additions. Pseudonyms are used to protect the identity of participants in the exchange. Findings Popper’s evolutionary analysis of learning and the Objective Knowledge Growth Framework provide a means of managing specific aspects of one’s education through engaging in this learning process. Although this approach to teaching and decision making takes time to master, it does not require reconstituting existing institutional arrangements before it can be implemented in hospitals. Instead, it asks medical students, teachers and practitioners to be open to the theoretical underpinnings of the approach and to view knowledge growth as a process of systematic trial and error elimination. Originality/value This paper is original in its conceptualisation and may well become a classic in education circles. It draws on Popper’s philosophical arguments and enters into a much needed discourse for teaching and learning.
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 | 0.040 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.074 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".