Survey of Special Collections and Archives in the United Kingdom and Ireland
Bibliographic record
Abstract
Special collections and archives play a key role in the future of research libraries. However, significant challenges face institutions that wish to capitalize on that value, to leverage and make fully available the rich content in special collections in order to support research, teaching, and community engagement.This report, produced in collaboration by OCLC Research and RLUK, builds on the foundation established by Taking Our Pulse: The OCLC Research Survey of Special Collections and Archives, a report published in 2010 that provides a rigorous, evidence-based appraisal of the state of special collections in the US and Canada. The survey provides both evidence and a basis for action as part of the RLUK's Unique and Distinctive Collections workstrand and OCLC Research's Mobilizing Unique Materials theme.This report provides institutional leaders, curators, special collections staff, and archivists both evidence and inspiration to plan for much needed and deserved transformation of special collections. Specifically, it contains twenty recommendations that the authors feel will have a positive impact toward addressing the issues identified. It also provides a backdrop for continued discussion, both within special collections and the larger library enterprise, for the role of special collections in an evolved information economy. These key findings and recommendations are highlighted in the report's executive summary, which has been published as a separate document for your reading convenience.Key findings:The top challenges for archives and special collections in the UK and Ireland are outreach, born-digital materials and space.Alignment of special collections with institutional missions and priorities is an ongoing challenge.The special collections sector is undergoing a major culture shift that mandates significant retraining and careful examination of priorities.Philanthropic support is limited, as are librarians' fundraising skills.Use of all types of special collections material has increased across the board.Users expect everything in libraries and archives to be digitized.One-third of archival collections are not discoverable in online catalogs.Management of born-digital archival materials remains in its infancy.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".