Bibliographic record
Abstract
This paper attempts to delineate a 20th century movement towards the formation of a truly 'deductive' metabiology. The aim is to reify and crystallize the historical reality of this new meta-biological ‘movement’ by suggesting that what essentially united it was an ontological or epistemically heuristic commitment to a biological vision whose grammar and syntax were provided by utilising non-classical forms of qualitative rather than quantitative mathematical modelling and expression. The paper primarily focusses on the anti-Darwinian metabiology of the Canadian born, yet UK domiciled, biologist Brian Goodwin, as well as comparing and contrasting his ideas withose those of other metabiological such as Per Alberch, Hal Waddington, Rene Thom and Vladimir Vernadsky. It is the contention of his paper that, historically, the purest expression and the philosophical consummation of many of the meta-biological ideas propounded by Goodwin and others discussed in this paper can actually be found in the extraordinary metaphysical vision of Benedict de Spinoza (1632-1677) particularly as expressed in his posthumously published masterpiece the Ethics (1677), and so this paper incorporates an account of the meta-biological relevance of Spinoza’s thinking in relation to these more modern metabiological thinkers, not in order to indicate direct influence, but as an attempt to reveal the potential metabiological inspiration of such an metaphysical hermeneutic for those concerned to increasingly understand the phenomenon of 'life' in deductive terms.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.049 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".