As ‘synapses’ become ‘protoplasmic kisses’: Symmetry across clinical reasoning and poetry
Bibliographic record
Abstract
CONTEXT: We argue that biomedicine at root is not primarily instrumental, but shares aesthetic, ethical and political values with poetry. Yet an instrumentalist bias in medical pedagogy can lead to frustration of biomedicine's potential. Such unfulfilled potential is exposed when making a comparison with poetry, a knowledge system that expressly engages a range of value systems. How then to recover biomedical language's riches for medical education's gain? METHODS: We combine scientific and artistic approaches by positing a common frame to which both medicine and poetry can aspire: the 'high-water mark' of language. Poetry's language is complex, intensive and connotative-concerned with mood, ambiguity, metaphor and embodiment. Biomedicine potentially engages with such linguistic complexities, particularly in metaphor production, yet persistently falls away from this high-water mark of language, reducing connotative language to denotation or literal meanings. We describe such instances of frustrated potential as 'trying to accelerate with the brake on'. This paradoxical state has become habitual in medical education. The resultant lack of productive metaphor insulates pedagogy from mood, separating it from the vernacular as a specialist tongue that ensures identification with the medical community of practice. Such language can alienate both patients and poets for the same reason: it is less human than technical. CONCLUSIONS: Using the example of clinical reasoning and attendant diagnostic work, we show that reductions from the connotative to the denotative not only mask but also contradict the complexity of implicit, embedded and distributed cognitive structures, creating a tension that medical education consistently fails to either resolve or draw upon as a resource. Further, poetry too has a complex set of implicit rules and formative structures that shape composition. These structures show symmetry, correspondence or even isomorphism with medical cognition, where both can aspire to activity that is aesthetically rich, intense and cognitively elegant.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.070 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".