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Record W2798649593 · doi:10.1109/vsmm.2017.8346256

Reality recalled: Elders, memory and VR

2017· article· en· W2798649593 on OpenAlexaff
Martha Ladly, Thoreau Bakker, Kartikay Chadha, Glen Farrelly, Katie Micak, Gerald Penn, Frank Rudzicz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsUniversity of TorontoOntario College of Art and Design
Fundersnot available
KeywordsVirtual realityComputer scienceHuman–computer interactionCognitive psychologyPsychology

Abstract

fetched live from OpenAlex

Reality Recalled explores memory, embodiment, and social interactions of elders and others with digital media experiences and VR. We advocate for a holistic view of the term ‘virtual’ in its conceptual categorization within technology, and make a case for enlarging the audiences and extending the benefits of virtualized realities. In our methods, we argue that design and research decisions should be predicated on inclusivity, usability and the pursuit of pleasurable collaborative meaning making. When human interaction takes place in ‘real’ and virtualized space, the mind is the ultimate virtual playground and memory acts as its controller. Through our research designs and testing with digital media projects, VR, and our current and prospective prototypes, we demonstrate the generation of new conceptual lenses, technological forms and experiences that may enrich the lives of elders, with potential benefits for other communities of participants.

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)0.003
metaresearch head score (Gemma)0.006
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.006
Scholarly communication0.0050.004
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.057
GPT teacher head0.322
Teacher spread0.266 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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