New APSA Council Members Elected
Bibliographic record
Abstract
Eight new members of the APSA Council have been elected in APSA's fourth contested election in recent years. The new Council members are: Lisa Baldez, Dartmouth College; Susan R. Burgess, Ohio University; Dennis Chong, Northwestern University; Michael W. Doyle, Columbia University; Kerry L. Haynie, Duke University; Arthur Lupia, University of Michigan; Anna Sampaio, University of Colorado, Denver; and Melissa S. Williams, University of Toronto. André Blais, Université de Montréal, was not elected. Under APSA election rules, the APSA Nominating Committee proposes one name per open seat and additional nominations, sponsored by at least 10 members, may be made from the membership. This year, Susan Burgess, Ohio University, was nominated from the membership and was elected. The election was conducted by electronic ballot, with a 31% turnout—similar to other recent APSA elections. Detailed results can be found at http://apsanet.org/section_710.cfm .
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.281 | 0.112 |
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".