When you don’t know what you don’t know: How two new collections librarians right-sized a collections budget
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Due to impending campus-wide downsizing, the Grand Valley State University (GVSU) Libraries projected that a worst-case scenario would result in a 14% cut to the library’s collections budget for fiscal year 2020. In the same year, GVSU Libraries welcomed several new members of its leadership team, including the dean, two associate deans, head of systems, head of collections, business administrator, and a vacancy after the long-time acquisitions manager retired. Budget cuts and staff turnover are tough, but they prompted a much-needed reassessment of roles, culture, and priorities in the library. Different approaches to spending and curating the library’s collections were vital to counteract the budgetary challenges. Cara Cadena, the new head of collections, was charged with building a task force to recommend cancellations and a plan to communicate these changes across campus. Decisions were made based on feedback gathered from teaching faculty, liaison librarians, campus stakeholders, and usage data. Ultimately, the communication plan proved to be the most critical--and challenging--part of the process. In this session, Cara and Marcia will discuss successes, missteps, results, the importance of vendor relationships, and future plans for collection management at GVSU. Attendees will gain insights into leveraging stakeholder buy-in and grasping opportunities amidst constant change (and decreased funding) in order to evolve effectively. They’ll also learn how GVSU Libraries are reimagining the role of the collections team.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 it