Data-Driven Decision Making: An Holistic Approach to Assessment in Special Collections Repositories
Bibliographic record
Abstract
Objective – In an environment of shrinking budgets and reduced staffing, this study seeks to identify a comprehensive, integrated assessment strategy to better focus diminished resources within special collections repositories. Methods – This article presents the results of a single case study conducted in the Special and Digital Collections department at a university library. The department created an holistic assessment model, taking into account both public and technical services, to explore inter-related questions affecting both day-to-day operations as well as long-term, strategic priorities. Results – Data from a variety of assessment activities positively impacted the department’s practices, informing decisions made about staff skill sets, training, and scheduling; outreach activities; and prioritizing technical services. The results provide a comprehensive view of both patron and department needs, allowing for a wide variety of improvements and changes in staffing practices, all driven by data rather than anecdotal evidence. Conclusion – Although the data generated for this study is institutionally specific, the methodology is applicable to special collections departments at other institutions. A systemic, holistic approach to assessment in special collections departments enables the implementation of operational efficiencies. It also provides data that allows the department to document its value to university-wide stakeholders.
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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 | 0.073 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.024 | 0.015 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".