Bibliographic record
Abstract
Analogical reasoning is central to medical progress, and is either creative or conservative. According to Hofmann et al., conservative analogy relates concepts from old technology to new technologies with emphasis on preservation of comprehension and conduct. Creative analogy however brings new understanding to new technology, brings similarities existing in the source domain to a target domain where they previously had no bearing, and imports something entirely different from the content of the analogy itself. I defend the claim that while conservative analogies are useful by virtue of being comfortable to use from familiarity and experience, and are more easily accepted by society, they only lead to incremental advances in medicine. However, creative analogies are more exciting and productive because they generate previously unexpected associations across widely separated domains, emphasize relations over physical similarities, and structure over superficiality. I use kidney transplantation and anti-rejection medication development as an exemplar of analogical reasoning used to improve medical practice. Anti-rejection medication has not helped highly sensitized patients because of their propensity to rejecting most organs. I outline how conservative analogical reasoning led to anti-rejection medication development, but creative analogical reasoning helped highly sensitized and blood type incompatible patients through domino transplants, by which they obtain a kidney to which they are not sensitized. Creative analogical reasoning is more likely than conservative analogical reasoning to lead to revolutionary progress. While these analogies overlap and creative analogies eventually become conservative, progress is best facilitated by combining conservative and creative analogical reasoning.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".