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Record W3149934921

Indians Playing Indian: Multiculturalism and Contemporary Indigenous Art in North America

2015· article· en· W3149934921 on OpenAlexaboutno aff
Monica Siebert

Bibliographic record

VenueProject Muse (Johns Hopkins University) · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsMulticulturalismIndigenousEthnologyEthnic groupAnthropologySociologyPolitical scienceGender studiesHistoryLaw
DOInot available

Abstract

fetched live from OpenAlex

"In Indians Playing Indian, Monika Siebert explores the appropriation, or misappropriation, of Native American cultural heritage for political and commercial ends, and the innovative ways in which indigenous artists in a range of media have responded to these developments. Contemporary indigenous people in North America confront a unique predicament. As legal and diplomatic practice in the early twenty first century returns to the recognition of their status as citizens of historic sovereign nations, popular culture continues to depict them as cultural minorities on the par with other ethnic Americans. This popular misperception of indigeneity as culture rather than as a historically developed political status sustains the myth of America as a refuge to the world's immigrants and a home to successful multicultural democracies. But it fundamentally misrepresents indigenous people who have experienced a history of colonization rather than a tradition of immigration on the continent. Contemporary indigenous cultural production is caught up in this phenomenon of multicultural misrecognition as well. The current flowering of indigenous literature, cinema, and visual arts is typically taken as evidence that Canada and the United States have successfully broken with their colonial pasts to become thriving nations of many cultures, where Native Americans, along other minorities, enjoy full freedom to represent their cultural difference"..

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.905
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.043
GPT teacher head0.223
Teacher spread0.180 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2015
Admission routes1
Has abstractyes

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