Technical Services Talk: Fostering Faculty Collaboration through Reorganization and Communication
Bibliographic record
Abstract
Do you wish you could get out from behind your desk and find out what patrons really want? Are you stuck staring at your computer screen wishing your department’s workflow could be more efficient and effective? If this sounds like you, come to this session to hear how one mid‐size technical services department (acquisitions, cataloging, serials, and e‐resources) at a regional public university of 6,000 students created a leaner, meaner, more focused unit by doing just that. By reorganizing our department and overhauling our workflow to take a more active role in the collection development process, we revitalized relationships with faculty and students to communicate and collaborate with faculty year‐round. Focused on small and mid‐size libraries, this session will teach attendees practical strategies to create more efficient workflows to better interact with users and hopefully save time and money in the process. Time will be built into the session for attendees to share about similar issues they have faced and their ideas on improving workflows and communication.
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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.053 | 0.019 |
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".