Using Immersive Planning Tools to Reimagine Virtual Libraries
Bibliographic record
Abstract
Immersive technologies (e.g., Virtual Reality) can both reproduce existing spaces or help bring imagination to life. When considered in relation to the needs of the users, these technologies can facilitate rewarding experiences that encourage repeated usage. However, poorly motivated experiences may result in expensive mistakes. One rewarding experience has been through the creation of immersive sound planning tools to help Professionals of the Built Environment (e.g. urban plannersand designers) consider sound in their work (Yanaky et al., 2023). Using a user-centered design process, we developed a Virtual Reality planning tool, City Ditty. A first evaluation indicated that users, regardless of their experience, could complete both a sound-awareness learning phase and implement their own soundscape designs in under an hour. Feedback was positive, suggesting value for its use in public consultations and participatory approaches towards creating healthier, inclusive, and sustainable communities. Could City Ditty be used to help rethink and prototype new forms of virtual libraries? Libraries host a wealth of information and contribute community space. They also act as community hubs for classes, games, storytelling, community events, etc. Yet, digitally reproducing a navigable 3D library space without consideration for the medium will reproduce the inconveniences of existing spaces, while failing to take advantage of the new medium. Could similar methodologies help engage library users to conceptualize together the future of virtual libraries? How might different users want to utilize immersive virtual libraries? We place this discussion in the context of a hype cycle for emerging technologies to understand potential timelines for change.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".