Progress in Gender Equality within the Realm of Scientific Academia Illustrated by the Career and Life of Neuroscientist Patricia Goldman-Rakic
Bibliographic record
Abstract
Gender inequality has been a constant struggle for women throughout history with victories few and far between. The movement for women’s rights emerged with the anti-slavery movement in the mid-1800s; however, it wasn’t until the late 1800s that women were allowed to grace the distinguished and revered lecture halls of higher education, and not until 1920 that women gained the right to formally matriculate and attain degrees. Upon commencement of women into the ranks of academia, the necessity to secure women’s rights for higher education appeared to be satiated. However, gender discrimination continued to plague particular fields of study, specifically the sciences, hindering explosive development and inhibiting potential innovative advancements. Although great progress has been made to increase the ranks of women in academia over the last couple decades, women remain underrepresented in faculty positions within scientific fields of study. The field of Neuroscience, although primarily male dominated, has had a few significant leading women pioneers who have courageously revolutionized the parameters of the science despite the strong opposition of the gender barriers. This paper aims to expose the underlying motives that perpetuate the gender bias in the field of neuroscience and explore the progress that has been made through the exemplary career and life of world-renowned and highly-respected neuroscientist Patricia Goldman-Rakic.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.023 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".