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Record W2886627917 · doi:10.1177/1206331218793065

Hybrid Space: An Emerging Opportunity That Alternative Reality Technologies Offer to the Museums

2018· article· en· W2886627917 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

VenueSpace and Culture · 2018
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsUniversity of Calgary
FundersUniversity of British Columbia
KeywordsVirtuality (gaming)Space (punctuation)Augmented realityVirtual realityMixed realityVirtual spaceSociologyComputer scienceHuman–computer interactionMultimediaEpistemologyArtificial intelligence

Abstract

fetched live from OpenAlex

In addition to the actual space and virtual space, there seems to be a third type that can be called hybrid space. Hybrid space borrows the power of information to empower the physical space around us using technologies such as augmented reality, virtual reality, and augmented virtuality. Hybrid space has been explored and conceptualized in the literature, but it has yet to reach its potential as an effective medium in museums. However, it seems to have quite a few advantages to be employed in the museums to attract more people, motivate a higher participation, and change the existing paradigms by reinventing museums. This article applies a qualitative content analysis to a sample of publications to conceptualize hybrid space and position it in a suggested continuum of space. Moreover, the role of technology and considerations about the museum content in a hybrid space are explored. The aim of this theoretically and technologically oriented article is to promote the professional use of the hybrid space in museums.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.961
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.054
GPT teacher head0.315
Teacher spread0.261 · 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