Hosting a Library Vendor Week: A Better Way to Manage Site Visits?
Bibliographic record
Abstract
Scheduling meetings between vendors and the appropriate library staff members is often a challenge, and the number of requests for site visits can quickly overwhelm any library calendar. The University Libraries at Virginia Tech recently held its first library vendor week in an attempt to address such concerns. Nearly two dozen vendors took part in the five-day event. This paper provides key lessons we learned during this experience and shares tips and strategies for libraries that may be interested in hosting their own multivendor event. With one perspective provided by the host library, and another from a vendor who took part, readers will learn from both sides about this uncommon approach to organizing vendor visits.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.033 | 0.014 |
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".