Discomfort, Doubt, and the Edge of Learning
Bibliographic record
Abstract
Discomfort is a constant presence in the practice of medicine and an oft-ignored feature of medical education. Nonetheless, if approached with thoughtfulness, patience, and understanding, discomfort may play a critical role in the education of physicians who practice with excellence, compassion, and justice. Taking Plato's notion of aporia-a moment of discomfort, perplexity, or impasse-as a starting point, the author follows the meandering path of aporia through Western philosophy and educational theory to argue for the importance of discomfort in opening up and orienting perspectives toward just and humanistic practice. Practical applications of this approach include problem-posing questions (from the work of Brazilian education theorist Paulo Freire), exercises to "make strange" beliefs and assumptions that are taken for granted, and the use of stories-especially stories without endings-all of which may prompt reflection and dialogical exchange. Framing this type of teaching and learning in Russian psychologist L.S. Vygotsky's theories of development, the author proposes that mentorship and dialogical interactions may help learners to navigate through moments of discomfort and uncertainty and extend the edge of learning. This approach may give birth to a zone of proximal development that is enriched with explorations of self, others, and the world.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.066 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".