Bibliographic record
Abstract
In recent years, evolutionary biology has been the focus of post-Darwinist theories superseding the mere notion of variation with a concept called evolutionary development. The theory of evolutionary development, commonly referred to as evo-devo, follows a series of observations on the nature of organic developments and natural morphologies. Its main contribution rests on an evolutionary model that considers the similarities of genetic material forming organisms and their differences in morphological development due to switching mechanisms between the assigned genes. As observed by the American biologist Sean Carroll, evolution follows regulatory sequences of selector genes that are similar and can be found across various species of insects, plants and animals. ¶ This observation represents a counter-proposal to the old-modern evolutionary theories that looked at processes of adaptation as a function of the emergence of new genes. Evo-devo, on the contrary, recognizes that morphological differences are triggered by recombinatory switches that re-arrange genes in manifold ways to produce numerous characteristics of adaptation. ¶ From a design point of view, evo-devo has tremendous implications because it suggests that generative design protocols may induce sets of similar operations, yet stimulate a wide range of morphologies according to their sequential arrangements and activities. These generative design strategies include, among others, computational methods such as structural shape annealing and object-oriented analysis and design. While these methods are now integrating computing design practices, it is here proposed to review these two computational design methods in the context of three research projects.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".