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Record W2327121349 · doi:10.1177/1469605311417064

‘Breaking the fourth wall’: 3D virtual worlds as tools for knowledge repatriation in archaeology

2011· article· en· W2327121349 on OpenAlexaffabout
Peter Dawson, Richard Levy, Natasha Lyons

Bibliographic record

VenueJournal of Social Archaeology · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsSimon Fraser UniversityUniversity of Calgary
FundersEuropean Science Foundation
KeywordsRepatriationIndigenousArchaeologyInterpretation (philosophy)AnthropologyTraditional knowledgeSociologyHistoryComputer science

Abstract

fetched live from OpenAlex

Interactive 3-dimensional worlds and computer modeling can be used to excite interest in archaeology among indigenous groups such as the Inuit of the North American Arctic and Greenland. Using two case studies – a recently completed exhibition for the Virtual Museum of Canada on Thule Inuit whalebone houses and an interactive virtual world structured around the Siglit-Inuvialuit sod house – we explore how digital replicas might be used in the repatriation of traditional knowledge. This idea is examined through theexperiences of nine Inuit Elders who explored our digital reconstructions of Thule and Siglit-Inuvialuit dwellings in 3D. Discussions with the Elders suggest that the generic sense of ‘presence’ generated by 3D viewing enhanced their feelings of connectedness to their past. This would imply that virtual reality and 3D technology might be useful in establishing new discourses in archaeological interpretation, as well as assisting in the exploration, construction, and maintenance of cultural identities through knowledge repatriation.

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)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0070.022
Scholarly communication0.0100.009
Open science0.0020.012
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.081
GPT teacher head0.282
Teacher spread0.202 · 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 designNot applicable
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

Citations60
Published2011
Admission routes2
Has abstractyes

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