Gauging Academic Unit Perceptions of Library Services During a Transition in University Budget Models
Bibliographic record
Abstract
Objective – Beginning in Fiscal Year 2023, a university initiated a multi-year transition to an incentive-based budget model, under which the University Libraries budget would eventually be dependent upon yearly contributions from colleges. Such a change could result in the colleges having a more profound interest in library services and resources. In anticipation of any changes in thoughts and perceptions on existing University Libraries services, researchers crafted a survey for administrators, faculty, and staff focused on academic units related to the health sciences. The collected information would inform library budget decisions with the goal of optimizing support for research and educational interests. Methods – An acquisitions and collection management librarian, electronic resources librarian, two health science liaisons, and a staff member reviewed and considered distributing validated surveys to health science faculty, staff, and administrators. Ultimately, researchers concluded that a local survey would allow the University Libraries to address health science community needs and gauge use of library services. In late October 2022, the researchers obtained Institutional Review Board approval and distributed the online survey from mid-November to mid-December 2022. Results – This survey collected 112 responses from health science administrators, faculty, and staff. Many faculty and staff members had used University Libraries services for more than 16 years. By contrast, most administrators started using the library within the past six years. Cost-share agreements intrigued participants as mechanisms for maintaining existing subscriptions or paying for new databases and e-journals. Most participants supported improving immediate access to full-text articles instead of relying on interlibrary loans. Participants desired to build upon existing knowledge of Open Access publishing. Results revealed inefficiencies in how the library communicates changes in collections (e.g., journals, books) and services. Conclusion – A report of the study findings sent to library administration fulfilled the research aim to inform budget decision making. With the possibility of reduced funds under the new internal budgeting model to both academic programs and the library, the study supports consideration of internal cost-sharing agreements. Findings exposed the lack of awareness of the library’s efforts at decision making transparency, which requires exploration of alternative communication methods. Research findings also revealed awareness of Open Educational Resources and Open Access publishing as areas that deserve heightened promotional efforts from librarians. Finally, this local survey and methodology provides a template for potential use at other institutions.
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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 | 0.031 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".