Bibliographic record
Abstract
Abstract Cover Story Prepare to ensure compliance with new ‘gainful employment’ rules ‘Gainful employment’ regulations will bring compliance burden Regulations provide flexibility to improve failing programs Briefing Group of for‐profits adopts conduct code Adjuncts at NYU win pay increases Walden grad programs add specializations Transitions Help students with the cost of textbooks What Would You Do? Competitor's tuition discounting causes concern Compliance ‘Flexible’ standard clarified for ‘credit hour’ definition ED issues guidance on regulations for program integrity, distance ed, incentive compensation Legislative Watch Legislative Watch Managing Your Office Learn to see problems from various perspectives to find effective solutions Lawsuits & Rulings STUDENT CONDUCT Appeals court affirms sanctions against grad student SEXUAL HARASSMENT Grad student‐worker can't find relief from harassment DISCRIMINATION OCR finds fault with response to grad student's accommodation request Leaders & Innovators GERVAN FEARON, DEAN, G. RAYMOND CHANG SCHOOL OF CONTINUING EDUCATION, RYERSON UNIVERSITY Collaboration can yield new programs, higher enrollments 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 | 0.003 |
| 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) | 0.888 | 0.791 |
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".