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Record W4401954965 · doi:10.1080/10447318.2024.2392967

Inclusion in Virtual Reality Technology: A Scoping Review

2024· review· en· W4401954965 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Human-Computer Interaction · 2024
Typereview
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsDalhousie UniversityCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsInclusion (mineral)Virtual realityPsychologyComputer scienceSociologyHuman–computer interactionSocial science

Abstract

fetched live from OpenAlex

Despite the significant growth in virtual reality applications and research, the notion of inclusion in virtual reality is not well studied. Inclusion refers to the active involvement of different groups of people in the adoption, use, design, and development of Virtual Reality (VR) technology and applications. In this review, we provide a scoping analysis of existing virtual reality research literature about inclusion. We categorize the literature based on target group into ability, gender, and age, followed by those that study community-based design of VR experiences. In the latter group, we focus mainly on Indigenous Peoples as a clearer and more important example. We also briefly review the approaches to model and consider the role of users in technology adoption and design as a background for inclusion studies. We identify a series of generic barriers and research gaps and some specific ones for each group, resulting in suggested directions for future research.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.093
GPT teacher head0.469
Teacher spread0.376 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it