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 .
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".