3D/VR in the Academic Library: Emerging Practices and Trends
Bibliographic record
Abstract
This volume, comprising eight chapters from experts in a variety of fields, examines the use of three-dimensional (3D) and virtual reality (VR) technologies in research and teaching, and the library’s vital role in supporting this work. 3D modeling, 3D capture techniques, and VR enable faculty and students to engage with highly detailed 3D data—from cultural heritage artifacts to scientific simulations—in new ways. As 3D and VR projects scale up and move outside of the specialist disciplines where they have existed for decades, many academic libraries are taking the lead in supporting such projects because they are already centers for collaboration, instruction, research, and collection preservation. The volume seeks to prompt greater awareness for library professionals as they develop programs that use 3D and VR technologies and work to integrate changing scholarly demands and conventions with existing library services and policies. Chapters cover 3D content creation, VR visualization and analysis, 3D/VR-based educational deployment, and 3D/VR data curation, providing a snapshot of professional objectives and workflows that have developed around 3D/VR.
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.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.021 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.028 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".