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Record W2587294794

Artifacts on Air: Cultural Coherence, Collaboration, and Remote Access in Indigenous Archeological Collections

2016· dissertation· en· W2587294794 on OpenAlexfundno aff
Emily Myfanwy Meikle

Bibliographic record

VenueTSpace (University of Toronto) · 2016
Typedissertation
Languageen
FieldSocial Sciences
TopicMedia, Communication, and Education
Canadian institutionsnot available
FundersMcMaster University
KeywordsIndigenousNarrativeDescendantInterpretation (philosophy)Cultural heritageSpace (punctuation)GlobalizationVisual artsCoherence (philosophical gambling strategy)Media studiesGeographySociologyHistoryArchaeologyArtComputer sciencePolitical scienceLiterature
DOInot available

Abstract

fetched live from OpenAlex

This thesis explores if and how radio can be used to promote remote access to Indigenous\narchaeological collections for descendant communities, with attention to how First Nations media techniques can inform museum interpretation. With increasing globalization, museums can now reach audiences who may never enter the museumâ s physical space. This is especially important for First Nations with a strong cultural interest in museum collections they are often unable to visit. How then, can we interpret Indigenous objects in a culturally coherent manner without a physical encounter? Within this, how can competing expert narratives be navigated through collaborative practice? This thesis acknowledges the insufficiencies of visual media for remotely interpreting Indigenous material heritage. Audio is proposed as a supplementary medium, which offers alternative interpretive benefits and is more broadly accessible. The radio format is also used to consider tensions between the presence and absence of objects and people.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.631
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.350
Teacher spread0.313 · 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 teacher head, not a consensus.

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

Citations0
Published2016
Admission routes1
Has abstractyes

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