Librarian perspectives on the role of virtual reality in public libraries
Bibliographic record
Abstract
Abstract This paper explores librarians’ perception of virtual reality as well as opportunities and challenges associated with implementing VR in public libraries. We interviewed 36 librarians who developed and offered VR programs as part of the research project, before and after the programming. The findings show how VR may be used in the public library as a learning tool and technology that encourages social interactions. Librarians discuss how the unique qualities of VR such as immersion and interactivity of VR makes it different from other digital media and present a different set of potential challenges when offered in the library. Librarians observed that while VR has a lot of potential as a technology for learning and social engagement, the success also largely depends on the VR content as well as the librarian's strategy for recruitment and promotion. In addition, we found that librarians had different understandings of what constitutes learning as well as how marginalized communities might benefit from this technology. The librarians we interviewed faced many challenges, however, our analysis of their experiences offers insight into designing successful VR programming in public libraries.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".