The agential perspective: Countermapping the modern synthesis
Bibliographic record
Abstract
We compare and contrast two theoretical perspectives on adaptive evolution-the orthodox Modern Synthesis perspective, and the nascent Agential Perspective. To do so, we develop the idea from Rasmus Grønfeldt Winther of a 'countermap', as a means for comparing the respective ontologies of different scientific perspectives. We conclude that the modern Synthesis perspective achieves an impressively comprehensive view of a universal set of dynamical properties of populations, but at the considerable cost of radically distorting the nature of the biological processes that contribute to evolution. For its part, the Agential Perspective offers the prospect of representing the biological processes of evolution with much greater fidelity, but at the expense of generality. Trade-offs of this sort are endemic to science, and inevitable. Recognizing them helps us to avoid the pitfalls of 'illicit reification', i.e. the mistake of interpreting a feature of a scientific perspective as a feature of the non-perspectival world. We argue that much of the traditional Modern Synthesis representation of the biology of evolution commits this illicit reification.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".