Perceptions and experiences of virtual reality in public libraries
Bibliographic record
Abstract
Purpose Virtual reality (VR) is becoming a more available technology including in public spaces like libraries. The value and role of VR as a tool for learning and social engagement are unclear. The purpose of this paper is to explore the ways in which library patrons and librarians perceive VR and experience VR through library drop-in programs. Design/methodology/approach This paper is based on research conducted in seven Washington State Libraries where VR was adopted for drop-in programming for the first time. Data was collected between March and June 2018 and involved interviews with librarians and patrons, a patron user experience survey, and observational field notes from researchers on site during library programs. Findings Findings are presented in relation to user perceptions of VR compared to their actual VR experiences, and in relation to informal learning and social engagements. The authors frame the analysis and discussion in relation to sociotechnical imaginaries – culturally situated ideas about the relationship between society and technology, and considering the larger cultural landscape that informs collective views about the present and future. Social implications The paper discusses pending and potential inequalities related to gender, race and class in conversation with technology industry and VR. Issues discussed include unequal access to technology in public libraries and representation of minoritized groups in VR. Originality/value This work takes a critical perspective considering the inequities in relation to mainstreaming VR through public spaces like 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".