Going Local: Creating Unique and Special Collections in an Academic Library
Bibliographic record
Abstract
Over the past two years, the University of British Columbia–Okanagan Library has undertaken a review to update their special collections and focus on the local geographical areas and targeted populations. From this, a localized, accessible, and unique collection has emerged that can better serve the students and faculty on campus, as well as community user groups in the area. This project helped to grow the community engagement focused strategic direction of the university and increase the visibility of the library in the surrounding community through building new relationships. This paper will focus on examining the roles libraries can play in developing targeted and focused special collections, drawing from recent experience in reimagining and expanding an existing special collections section within a newer academic campus library. It is hoped that this paper can spark considerations of the impact special collections can have on the strategic goals of a library or university or college, and on the roles and responsibilities academic libraries have in preserving local history. Preface: A version of the history, project overview, and results section has been published by the Okanagan Historical Society. Berringer, H., Gattrell, J., & Lomness, A. (2015). Special collections at the University of British Columbia’s Okanagan Campus Library: Expanding our community’s access to local history. In D. Gregory (Ed.), Okanagan history: The seventy‐ninth report of the Okanagan Historical Society (pp. 106–113). Kelowna, BC: Okanagan Historical Society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.034 | 0.013 |
| Scholarly communication | 0.025 | 0.024 |
| Open science | 0.004 | 0.034 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".