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Record W2465297518 · doi:10.1145/2893182

Accessible Play in Everyday Spaces

2016· article· en· W2465297518 on OpenAlexafffund
Katie Seaborn, Jamal K. Edey, Gregory Dolinar, Margot Whitfield, Paula Gardner, Carmen Branje, Deborah I. Fels

Bibliographic record

VenueACM Transactions on Computer-Human Interaction · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsToronto Metropolitan UniversityCentennial CollegeBrock UniversityUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEntertainmentMixed realityEveryday lifeVirtual realityComputer scienceVariety (cybernetics)MultimediaHuman–computer interactionSpace (punctuation)Exploratory researchField (mathematics)Set (abstract data type)SociologyVisual artsPolitical scienceArtArtificial intelligence

Abstract

fetched live from OpenAlex

The advent of affordable and powerful mobile technology has allowed for explorations in mixed reality that merges virtual and physical space. However, the social and entertainment value and efficacy of mixed reality platforms for adult powered chair users has not been widely explored. In this article, we introduce the Mobility Games project, which aims to produce a series of inclusive entertainment technologies and services for people who use powered chairs. We describe our first offering: an accessible, social mixed reality game for co-located mobile play in everyday spaces. Findings from two exploratory field studies and a post hoc observer survey show that adult powered chair users found the game to be entertaining and used a variety of path strategies as they learned to play the game. An initial set of theoretically and empirically informed guidelines for making mobile mixed reality games accessible to adult powered chair users with diverse abilities is proposed.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.003
Scholarly communication0.0040.004
Open science0.0010.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.032
GPT teacher head0.338
Teacher spread0.306 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations26
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueACM Transactions on Computer-Human InteractionSame topicTechnology Use by Older AdultsFrench-language works237,207