Limits of imagination: the 150th Anniversary of Mendel’s Laws, and why Mendel failed to see the importance of his discovery for Darwin’s theory of evolution
Bibliographic record
Abstract
Mendel is credited for discovering Laws of Heredity, but his work has come under criticism on three grounds: for possible falsification of data to fit his expectations, for getting undue credit for the laws of heredity without having ideas of segregation and independent assortment, and for being interested in the development of hybrids rather than in the laws of heredity. I present a brief review of these criticisms and conclude that Mendel deserved to be called the father of genetics even if he may not, and most likely did not, have clear ideas of segregation and particulate determiners as we know them now. I argue that neither Mendel understood the evolutionary significance of his findings for the problem of genetic variation, nor would Darwin have understood their significance had he read Mendel's paper. I argue that the limits to imagination, in both cases, came from their mental framework being shaped by existing paradigms-blending inheritance in the case of Darwin, hybrid development in the case of Mendel. Like Einstein, Darwin's natural selection was deterministic; like Niels Bohr, Mendel's Laws were probabilistic-based on random segregation of trait-determining "factors". Unlike Einstein who understood quantum mechanics, Darwin would have been at a loss with Mendel's paper with no guide to turn to. Geniuses in their imaginations are like heat-seeking missiles locked-in with their targets of deep interests and they generally see things in one dimension only. Imagination has limits; unaided imagination is like a bird without wings--it goes nowhere.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".