Bibliographic record
Abstract
Women in coevolution 2022While women have been and continue to be underrepresented in science (World Economic Forum, 2023; UNESCO, 2024), they have been integral to advancing the field of evolution as well as the subfield of coevolution, the topic of this thematic collection of articles.Since natural selection was first theorized, Charles Darwin illustrated the selection pressures imposed not only by the environment, but also by interactions between species resulting in reciprocal evolution, i.e. coevolution.In particular, he famously predicted the existence of a species of moth that must possess an unusually long proboscis to obtain nectar at the end of the long floral spur from the orchid Angraecum sesquipedale (Darwin, 1862, pp.197-203;Arditti et al., 2012).Indeed, the moth predicted to pollinate A. sesquipedale does in fact exist, formally described in 1903 as Xanthopan morganii praedicta, thus demonstrating the predictive power of coevolutionary theory (Arditti et al., 2012).Systemic biases, however, have led to research in evolution to be largely dominated by men (Wellenreuther and Otto, 2016), and consequently, the topics or ideas that have been investigated have also been tackled through a male-biased lens (Keller, 2004;Ah-King, 2022).Despite the historical lack of opportunity, resources, and recognition, women have made outstanding contributions to coevolution.A female pioneer in this field is Lynn Margulis.She revived the symbiogenesis theory, that organelles such as mitochondria and chloroplasts are descendants of free-living independent prokaryotes that evolved reductively, to become obligate intracellular symbionts (endosymbionts) of a proto-eukaryotic cell (Sagan, 1967).Initially, the theory, based on physiological and structural evidence, was widely rejected, but Margulis's intuitions on an endosymbiotic origin were eventually proven with the added support of genetic evidence (Schwartz and Dayhoff, 1978;Gray and Doolittle, 1982).This episode opened our minds to the expanse of possible paths that evolution can take.In keeping to our theme, transformative actions supporting women in science will also contribute towards a positive coevolution between genders in our societies, requiring all genders to work cooperatively, and reciprocally, to explore and find solutions for the critical challenges impacting our collective futures, such as climate change and sustainable resource use (Nature editorial, 2022;Yang et al., 2022).The voice, actions and thoughts of women in science are needed for the evolutive success and happiness of humankind.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.039 | 0.025 |
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".