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Record W1600646000 · doi:10.20919/exs.5.2014.184

Alternative Narrative Forms, Exposure, and the Limits of Formalized Truth-Telling: Giving Accounts Though New Methods in Indigenous Art

2020· article· en· W1600646000 on OpenAlexaffabout
Michelle Siobhan O'Brien

Bibliographic record

VenueExcursions Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIndigenousNarrativeSociologyAestheticsAppropriationPower (physics)Variety (cybernetics)HistoryEpistemologyArtLiteraturePhilosophy

Abstract

fetched live from OpenAlex

This paper examines how individual truths concerning the atrocities and ruptures in Indigenous history, and ongoing cultural continuity in Indigenous society (despite these occurrences) can be located in current movements in Indigenous artwork. It draws upon both Judith Butler’s work on giving an account of oneself and Foucault’s notion of parrhesia to provide a frame for this engagement, and to argue for innovations in Indigenous art as indicative of methods of giving personal accounts and truth-telling that exceed the containable narratives of formal documentation. Through examining new interventions by Indigenous artists—the performance art work of Anishinaabe Canadian artist Rebecca Belmore, and the multimedia work of Kevin Lee Burton, who is Swampy-Cree—it identifies their works as exemplary of how Indigenous artistic interventions continue to formulate new methods of speaking truth to power grounded in cultural-specific forms of narrating personal truths by incorporating a variety of media and emphasize interactivity in their work. This paper ultimately argues that in the creation of art that shares personal truths and give these accounts while also acknowledging narrative absences and gaps, these artists convey the possibility of Indigenous art to share truths that might not otherwise be acknowledged by official historical record.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
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 head0.055
GPT teacher head0.395
Teacher spread0.340 · 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
Published2020
Admission routes2
Has abstractyes

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