Conceptual Roles of Evolvability across Evolutionary Biology: Between Diversity and Unification
Bibliographic record
Abstract
Biologists and phi los o phers have noted the diversity of interpretations of evolvability in con temporary evolutionary research. Dif fer ent clusters of research identified by co-citation patterns or shared methodological orientation sometimes concentrate on distinct conceptions of evolvability. We examine five dif fer ent activities where the notion of evolvability plays conceptual roles in evolutionary biological investigation: setting a research agenda, characterization, explanation, prediction, and control. Our analy sis of representative examples demonstrates how dif fer ent conceptual roles of evolvability are quasi-independent and yet exhibit impor tant relationships across scientific activities. It also provides resources to detail two distinct strategies for how evolvability can help synthesize disparate areas of research and thereby potentially serve as a unifying concept in evolutionary biology.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.004 | 0.069 |
| Scholarly communication | 0.014 | 0.027 |
| Open science | 0.003 | 0.010 |
| 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".