Editorial: Women in neuroscience
Bibliographic record
Abstract
The “Matilda effect” is an expression coined in 1993 by Margaret Rossiter, a prominent science historian, to describe the faint recognition of the contribution of women to the scientific enterprise. The expression derived from the realization that just like the work of Matilda Gage, a suffragist who also wrote about women in science, the discoveries and inventions of many women scientists had been forgotten over the course of history. Indeed, women’s contributions to science have been often misappropriated, forgotten or, in some cases, even actively removed from the records. This resulted in a misplaced historic assumption that women lack the intellectual ability and interest for scientific disciplines, and left younger generations of women with very few role models to look up to. Over the past few years, the awareness of this lack of recognition has increased and, despite encountering some resistance, active efforts have been made to make science a more inclusive enterprise. Neuroscience is a multidisciplinary field that encompasses all scientific disciplines including biology, psychology, cognitive sciences, physics, engineering, and mathematics. While women to this day represent a minority of neuroscience faculty, they contribute to all aspects of the field. The goal of the Women in neuroscience Research Topic is to oppose the “Matilda effect” by bringing together excellent research by women, or in collaboration with women. The Research Topic brings together 33 articles in which the first or last author are women. The formats include mini-reviews and reviews of the exceptional work done by past and present women neuroscientists, an opinion article, perspectives and specific Research Topic reviews highlighting scholarship and innovative frameworks, and original research articles that push the field forward.
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 | 0.028 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.040 | 0.029 |
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".