Vendorbrarians: Librarians Who Work for Vendors and the Value They Provide to Library Customers
Bibliographic record
Abstract
A panel of librarians working for different kinds of library vendors discussed their unique and valuable roles inside their organizations. The session was moderated by an Electronic Resources Librarian with an interest in library/vendor relationships. Librarians can add value to their company’s relationships with library customers as they share the same basic skill set as their colleagues in libraries and have a better understanding of their needs, industry standards, and the day to day realities of their customers. Topics discussed included the kinds of roles librarians can have at vendors, how these positions compare and contrast with more traditional library work, their identities in the library profession, how library school did and did not prepare them for their jobs, and more.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".