Perceptions and experiences of virtual reality in public libraries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it