Inequity in authorship of North American lichens
Bibliographic record
Abstract
Women have long been underrepresented in the sciences, and their contributions are often overlooked. Previous work has demonstrated a significant productivity gap between men and women when investigating vascular plant authorities and the naming of plant taxa. No study has directly investigated gender inequity, as depicted through authority identity, in the field of lichenology. Our research goal was to describe patterns in gender identity and country of origin for authors of North American lichens. We compiled and analyzed information from the North American Lichen Checklist (including U.S.A. and Canada but not Mexico), independent research, and a gender API to identify the full name, suspected gender, birth year, and country of origin of 889 authors (i.e., people listed as authorities of North American lichen taxa). Of the total 4,895 unique lichen taxa in North America, only 3.2% species were named by a woman. Even standardized by co-authors, men authors contributed significantly more than women authors in this field. We also noted that most authors originated from Europe or the United States. This work suggests that the field of lichenology could provide more support systems for American or Canadian women to contribute naming of new taxa or combinations. While our work focused only on authoring new species as a contribution, we recognize that women may be contributing in other notable ways to lichenology in North America.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".