“What’s So Special about Special Collections?” Or, Assessing the Value Special Collections Bring to Academic Libraries
Bibliographic record
Abstract
Objective – The objective of this study was to examine and call attention to the current deficiency in standardized performance measures and usage metrics suited to assessing the value and impact of special collections and archives and their contributions to the mission of academic research libraries and to suggest possible approaches to overcoming the deficiency. Methods – The authors reviewed attempts over the past dozen years by the Association of Research Libraries (ARL) and the Association of College and Research Libraries (ACRL) to highlight the unique types of value that special collections and archival resources contribute to academic research libraries. They also examined the results of a large survey of special collections and archives conducted by OCLC Research in 2010. In addition, they investigated efforts by the Society of American Archivists (SAA) dating back to the 1940s to define standardized metrics for gathering and comparing data about archival operations. Finding that the library and archival communities have thus far failed to develop and adopt common metrics and methods for gathering data about the activities of special collections and archives, the authors explored the potential benefits of borrowing concepts for developing user-centered value propositions and metrics from the business community. Results – This study found that there has been a lack of consensus and precision concerning the definition of “special collections” and the value propositions they offer, and that most attempts have been limited in their usefulness because they were collections-centric. The study likewise reaffirmed a lack of consensus regarding how to define and measure basic operations performed by special collections and archives, such as circulating materials to users in supervised reading rooms. The review of concepts and metrics for assessing value in the business community, however, suggested new approaches to defining metrics that may be more successful. Conclusion – The authors recommend shifting from collection-centric to user-centric approaches and identifying appropriately precise metrics that can be consistently and widely applied to facilitate cross-institutional comparisons. Adopting a user-centric perspective, they argue, will provide a broader picture of how scholars interact with special collections at different points in the research process, both inside and outside of supervised reading rooms, as well as how undergraduate students change their thinking about evidence through interaction with primary sources. They authors outline the potential benefits of substituting the commonly used “reader-day” metric for tabulating reading room visits with a “reader-hour” metric and correlating it with item usage data in order to gauge the intensity of reading room use. They also discuss the potential benefits of assessing impact of instructional outreach in special collections and archives through measures of student confidence in pursuing research projects that involve primary sources.
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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 | 0.033 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.022 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.026 | 0.034 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".