Showcasing Institutional Research: Curating Library Exhibits to Support Scholarly Communication
Bibliographic record
Abstract
INTRODUCTION To support faculty in communicating their research outcomes to the academic community and the wider public, the Royal Roads University (RRU) Library established Showcase, a physical venue in the library designed to promote institutional research. While professional literature mainly focuses on the use of library exhibits for outreach and community engagement, more literature is needed on applying museum interpretation practices to the development of library exhibits, and the use of library exhibits for knowledge mobilization of research outcomes and promotion of institutional scholarship to the wider community. DESCRIPTION OF SERVICE This article discusses the Royal Roads University Library’s practices to develop the ‘Showcase’ brand by curating research-based exhibits as a scholarly communication initiative to support institutional research dissemination. It provides a brief description of the Showcase venue and infrastructure. It then describes the processes, challenges, and lessons learned in developing three research exhibits, that is, 1) cultivating faculty partnerships; 2) reformatting academic research to multimedia formats; and 3) integrating technology to showcase scholarship. NEXT STEPS It concludes by outlining the next steps for developing this initiative and the practice of curating academic research exhibits.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.008 |
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".