Where is, in 2017, the evo in evo‐devo (evolutionary developmental biology)?
Bibliographic record
Abstract
After the inaugural Pan-American-Evo-Devo meeting (2015, Berkeley), I showed how major concerns about evo-devo (Evolutionary Developmental Biology) research were demonstrated by a simple, non-biased quantitative analysis of the titles/abstracts of that meeting's talks. Here, I apply the same methodology to the titles/abstracts of the recent Pan-American-Evo-Devo meeting (2017, Calgary). The aim is to evaluate if the concerns raised by me in that paper and by other authors have been addressed and/or if there are other types of differences between the two meetings that may reflect trends within the field of evo-devo. This analysis shows that the proportion of presentations referring to "morphology", "organism", "selection", "adaptive", "phylogeny", and their derivatives was higher in the 2017 meeting, which therefore had a more "organismal" feel. However, there was a decrease in the use of "evolution"/its derivatives and of macroevolutionary terms related to the tempo and mode of evolution in the 2017 meeting. Moreover, the disproportionately high use of genetic/genomic terms clearly shows that evo-devo continues to be mainly focused on devo, and particularly on "Geno", that is, on molecular/genetic studies. Furthermore, the vast majority of animal evo-devo studies are focused only on hard tissues, which are just a small fraction of the whole organism-for example, only 15% of the tissue mass of the human body. The lack of an integrative approach is also evidenced by the lack of studies addressing conceptual/long-standing broader questions, including the links between ecology and particularly behavior and developmental/evolutionary variability and between evo-devo and evolutionary medicine.
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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.013 |
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".