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Record W4383683031 · doi:10.1145/3563657.3596005

Sharing Play Spaces: Design Lessons from Reddit Posts Showing Virtual Reality in the Home

2023· article· en· W4383683031 on OpenAlexafffund
Daniel Harley, Cayley MacArthur

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAffordanceHuman–computer interactionVirtual realityVariety (cybernetics)Set (abstract data type)RecreationComputer scienceMultimediaPolitical science

Abstract

fetched live from OpenAlex

With the increasing availability of consumer virtual reality (VR) devices for personal and recreational use, the domestic contexts of VR design are increasingly important. Given that much of the current interaction design research for VR is conducted in lab-based settings, there is a need for design considerations that engage with the complexities of these real-world spaces. We present an analysis of visual data (e.g., GIFs, videos, photographs) collected from a manual search of Reddit posts that show “play spaces” and other home-based contexts of VR. Our findings offer insight into the diverse and dynamic characteristics of VR spaces, with set-ups ranging from bedrooms to garages, and with the people, objects, impediments, and affordances of individual spaces demonstrating a variety of ways that VR is used in the home. We conclude by discussing directions for future interaction design research that seeks to incorporate physical actions and environments while also engaging with the complex realities of domestic 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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.841
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.000
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 head0.138
GPT teacher head0.339
Teacher spread0.201 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2023
Admission routes2
Has abstractyes

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