2022 AOS Early Professional Awards to Stepfanie M. Aguillon, Benjamin Freeman, and Allison Shultz
Bibliographic record
Abstract
Stepfanie M. Aguillon Benjamin Freeman The American Ornithological Society (AOS) is pleased to announce Stepfanie M. Aguillon, Benjamin Freeman, and Allison Shultz as the 2022 recipients of the society’s Early Professional Awards, the James G. Cooper Early Professional Award and the Ned K. Johnson Early Investigator Award. The James G. Cooper Early Professional Award and the Ned K. Johnson Early Investigator Award are presented annually to recognize outstanding and promising work by researchers early in their careers. The 2022 James G. Cooper Early Professional Award is presented to Dr. Stepfanie M. Aguillon, postdoctoral fellow at Stanford University. Dr. Aguillon, who received her Ph.D. from Cornell University, impressed the award committee with her mini-paper on the genetics of reproductive isolation, as well as her notable research accomplishments, including the publication of 11 peer-reviewed papers in renowned journals including The Auk, Proceedings B, Behavioral Ecology and Sociobiology, and PLOS Genetics. Dr. Aguillon is also a dedicated educator; her peer-reviewed publications include two papers on pedagogy. Dr. Aguillon has delivered excellent talks at AOS annual meetings and demonstrates leadership within the Society through her active engagement in AOS activities and her advocacy for strong mental health supports for graduate students.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.186 | 0.104 |
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".