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Record W2751688063 · doi:10.14236/ewic/eva2017.73

Embodied Interactions with a Sufi Dhikr Ritual: Negotiating Privacy and Transmission of Intangible Cultural Heritage in “Virtual Sama”

2017· article· en· W2751688063 on OpenAlex
Aynur Kadir, Kate Hennessy, Özge Nilay Yalçın, Steve DiPaola

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

VenueElectronic workshops in computing · 2017
Typearticle
Languageen
FieldComputer Science
TopicHuman Pose and Action Recognition
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsIntangible cultural heritageDocumentationContext (archaeology)Embodied cognitionCultural heritageSafeguardingNegotiationSociologyAnonymityAestheticsComputer scienceVisual artsArtHistorySocial scienceArtificial intelligenceArchaeology

Abstract

fetched live from OpenAlex

“Virtual Sama” is an interactive multimedia installation that connects computationally abstracted ethnographic documentation of a Sufi Dhikr ritual with viewers through an artistic artificial intelligence (AI) abstraction process and interactive rhythmic full body movement. In this paper, we describe how the installation is designed to elicit reflection on the implications of transforming intangible heritage into digital heritage through digital documentation and storage, and to encourage exploration of questions around privacy and safeguarding of sensitive cultural practices. Against the context of detailed fieldwork with Uyghur Sufi practitioners in Xinjiang, China, we explore how AI processes and embodied interaction might be mobilised to present alternative representations of anonymity, while drawing attention to the complexities of representation, access and transmission of intangible cultural practices in the digital age.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score0.593

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.287
Teacher spread0.270 · 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