Bibliographic record
Abstract
Abstract Cover Story Experts share their top tips for improving athletics departments Increase your effectiveness by following this advice The Bottom Line Prepare for new year The Two‐Minute Drill University tests students‐athletes for drug, alcohol use University football player faces rape charge Required tutorial covers sexual violence Canadian university presidents aim to keep athletes in Canada Leaders & Innovators DON DI JULIA, VICE PRESIDENT FOR ATHLETICS/ATHLETIC DIRECTOR, SAINT JOSEPH'S UNIVERSITY Balance various demands from multiple constituents Nurture relationships with NCAA, conference, stakeholders Liability Huge exit fees, lawsuits for conference realignment could be stemmed by changing media‐rights terms Professional Development Calendar Make your professional development plans now Compliance Determine if volunteers are school officials under FERPA Review relevant FERPA regs You Make The Call Did AD violate university's disciplinary procedures? Lawsuits & Rulings COACH CONDUCT Media circus triggered by trial may not require venue change FERPA Education record redactions must protect students' identity A Conversation With… LARRY ROPER, VICE PROVOST FOR STUDENT AFFAIRS, OREGON STATE UNIVERSITY Follow the energy to increase success Resources/Ideas Motivate athletes Promote health Names in the News
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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 | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.890 | 0.787 |
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 source (direct Gemma or distilled Codex), 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".