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Record W4318606435 · doi:10.1109/aivr56993.2022.00048

Representing Cross-Cultural Links of Artifacts in Museums with Augmented Reality

2022· article· en· W4318606435 on OpenAlex
Peiheng Zhao, Alexis Morris

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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsOntario College of Art and Design
Fundersnot available
KeywordsAugmented realityMuseologySpace (punctuation)ChinaPublic spaceProcess (computing)Computer scienceMultimediaVisual artsArtHuman–computer interactionGeographyArchitectural engineeringEngineeringArchaeology

Abstract

fetched live from OpenAlex

Museums play a significant role in collecting and displaying artifacts from different cultures. As public spaces, museums also represent the society we live in and affect the public understanding of the past. Meanwhile, emerging technology such as Augmented Reality (AR) is changing how we perceive information, including in the museum space. This paper explores how AR can better help museum visitors understand the intercultural link between artifacts, by using Blue-and-White porcelain from Ming China and Safavid Iran as a case study. An AR prototype was developed during the process. This paper concludes with a discussion of the potential for AR as a powerful tool to solve problems in the current museology approach.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.309

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 communication0.0000.000
Open science0.0010.001
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 head0.037
GPT teacher head0.327
Teacher spread0.290 · 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

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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