Conceptualizing Evolutionary Novelty: Moving Beyond Definitional Debates
Bibliographic record
Abstract
According to many biologists, explaining the evolution of morphological novelty and behavioral innovation are central endeavors in contemporary evolutionary biology. These endeavors are inherently multidisciplinary but also have involved a high degree of controversy. One key source of controversy is the definitional diversity associated with the concept of evolutionary novelty, which can lead to contradictory claims (a novel trait according to one definition is not a novel trait according to another). We argue that this diversity should be interpreted in light of a different epistemic role played by the concept of evolutionary novelty-the structuring of a problem space or setting of an explanatory agenda-rather than the concept's capacity to categorize traits as novel. This distinctive role is consistent with the definitional diversity and shows that the concept of novelty benefits ongoing investigation by focusing attention on answering different questions related to comprehending the origins of novelty. A review of recent theoretical and empirical work on evolutionary novelty confirms this interpretation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.005 | 0.018 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".