Bibliographic record
Abstract
Abstract Cover Story Learn what it takes to become college president Are you ready to be president? Expect presidency to be exhilarating News Briefing Report shows trends in tuition, aid AAUP investigates Antioch governance Institution develops grad rate alternative Faculty salaries in Canada ahead of those in U.S. Faculty alert media to salary problems Dean Dialogue Enlist business leaders to promote programs, fundraising Incorporate business principles into academics Strategic Planning Meet challenges economic downturn presents Executive Management Follow the Ceremony Season Diet, Version 1.5.2 Remember communication rules to keep events positive What Would You Do? How do you maintain morale through budget cuts? Lawsuits & Rulings ACADEMIC FREEDOM Funding provisions of Fla.'s Travel Act held unconstitutional DISABILITY Section 504 violation provides second chance at final exam SEXUAL HARASSMENT Bizarre conduct does not meet sexual harassment criteria Temple's sexual harassment policy held unconstitutional TENURE Extension to term of employment does not grant tenure Resources/Names in the News Review report on compliance costs for preventing illegal downloads Learn more about Michelle's Law from an analysis by HR experts Find out impact of health habits on students' GPAs NAMES IN THE NEWS Baylor provost chosen as Carson‐Newman president
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 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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.881 | 0.804 |
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; the direct Gemma label and the distilled Codex classifier 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".