Bibliographic record
Abstract
In April 2023, 564 people flocked to the University of Cincinnati (UC) to immerse themselves in math at the American Mathematical Society's spring meeting for the central section.They represented 42 US states and the District of Columbia.Other guests traveled from Canada (seven), Japan (three), and Haiti, Mexico, and Sweden (one each)."I heard from so many participants, many of whom hadn't traveled much in recent years, how important this opportunity to reconnect with colleagues was," said AMS Associate Secretary for the Central Section Betsy Stovall, professor of mathematics, University of Wisconsin-Madison.As one of four AMS associate secretaries, Stovall plans two sectional meetings per year.During two weekend days, 468 speakers presented 481 abstracts.Thirty special sessions were composed of 102 sub-sessions.Two contributed paper sessions were held, and four invited addresses took place."As a conference host, I'm just in shock at how much went on in the span of a single weekend," said Michael Goldberg, UC math department chair and professor."Then again, I've never thrown a party for 500 people before.""Fantastic weekend indeed," said Eyvindur Ari Palsson, associate professor of mathematics at Virginia Tech, who organized a special session and presented research in Cincinnati.We asked Palsson, Stovall, Goldberg, and other behindthe-scenes players for their advice to prospective hosts of sectional meetings.Here's what they had to say.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, 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".