What Provosts Think Librarians Should Know
Bibliographic record
Abstract
Provosts and librarians are naturally allies, but they live in different worlds and therefore may not always fully understand each other. This panel, chaired by one provost and featuring three others, aims to outline some of the main things Provosts are thinking about today that affect libraries and opens the floor for discussion on how to advance common interests. The presentation offers a diversity of experiences and institutional settings to broaden the scope of information. Topics include a mix of innovation in teaching and learning, support for research, funding challenges for different sectors of higher education, and the flurry of interest in online learning. Ultimately, the presentation sets out to link Provosts and librarians and help them understand how they can work together to make library systems more successful.
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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 | 0.021 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.040 | 0.048 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.047 | 0.030 |
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".