Bibliographic record
Abstract
Abstract Cover Story Apply policies consistently with an easy‐to‐understand format Clarify academic policies with Information Mapping® Consider these advantages of Information Mapping® Review differences between DeVry's old and new policy manuals News Briefing/Resources University guarantees its graduates House to consider bill blocking ED rules NYU, U of the People create partnership Stress, anxiety affect students' work Business majors need liberal arts Compliance Avoid 7 problems that can get your athletics department in trouble with the IRS Research Adopt a faculty‐student consensual relationship policy to protect students, faculty, institution Do you have a written policy for faculty‐student relationships? Is your policy a deterrent? Are consensual relationships a problem at your institution? Consider best‐practice implications What Would You Do? What would you do if alumni complained that they couldn't get jobs? Lawsuits & Rulings AGE DISCRIMINATION Exclusion from job‐related meetings supports plaintiff's retaliation claim GENDER DISCRIMINATION Single comment in dean's letter does not amount to adverse action FACULTY Failure to promote was not sufficient to state discrimination claim Focus on Leadership GERVAN FEARON, DEAN, G. RAYMOND CHANG SCHOOL OF CONTINUING EDUCATION, RYERSON UNIVERSITY Create new programs, boost enrollments through collaboration Help your team develop ownership for their unit
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 | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.891 | 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".