Data-Driven Decision Making: An Holistic Approach to Assessment in Special Collections Repositories
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
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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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.227 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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