Poisson models for publications, leadership and recognition awards in the American Academy of Neurology
Bibliographic record
Abstract
We sought to study sex with respect to publications, leadership and recognition awards in the American Academy of Neurology (AAN) in light of recent research highlighting inequities in these domains. Methods: We examined medical school graduation, neurology residency (using American Medical Association and the American Council for Graduate Medical Education), membership in the AAN, first and last authorship in Neurology, membership on AAN committees, and AAN recognition awards, by sex, in 1997, 2007, 2017. Results: Female medical students were less likely to enter neurology residency in 1997 only. In 2007 and 2017, there was no proportionate difference between men and women as last author, a surrogate for senior member of the author panel. In 2017, women were proportionately more likely to be first authors than men, a surrogate for principal investigator of the study. Committee membership was less for women in 1997 and 2007 (P <0.001) but was not proportionately different in 2017 (P=0.534). Women were proportionately more likely to receive recognition awards in all years studied (1997 p=0.008, 2007 p<0.001. 2017 p <0.001) although absolute numbers of women were less. Conclusions: Female membership, leadership (through committee membership), publications as last author were less in 1997 in the AAN. These same metrics demonstrated substantial proportionate changes, with no differences in last author in 2007 and 2017, greater likelihood for women to be first author in 2017, no differences in committee membership in 2017 and greater likelihood of receiving awards determined by merit in all three years.
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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 | 0.037 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".