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Record W4232881513 · doi:10.32920/ryerson.14662755.v1

Colonial costuming: representations of playing Indian in photographs, settler Colonialism and the appropriation of native North American culture

2021· preprint· en· W4232881513 on OpenAlexaffabout
Hikka Ingrid Forster

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicHistorical Gender and Feminism Studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsWhite (mutation)ColonialismAppropriationPortraitContext (archaeology)Cultural appropriationHistoryIdeologyIndigenousVisual artsMasculinityArtAnthropologyGender studiesSociologyPoliticsArchaeologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

This thesis examines the cultural significance of “playing Indian” in photographs: the practice of non-Native peoples dressing up in Native North American costumes and posing for photographs. It addresses photographs made both inside and outside the studio of people playing Indian, during both the later part of the nineteenth and early twentieth centuries and looks at the extent to which these photographs reinforce settler colonial ideology prevalent within white society during this time period. Examples from two collections will be explored, portraits from the Notman photography collection at the McCord museum, which includes examples of white Europeans and North Americans dressing up in Native “costumes” and photographs of children playing Indian in the First Nations collection at the Archive of Modern Conflict Toronto. Themes of masculinity, nation-building, “Canadianness,” and childhood in relation to indigeneity are explored by situating the photographs within their historical and cultural context and subsequently relating them to the already existing theories on playing Indian.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.015
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.024
GPT teacher head0.318
Teacher spread0.294 · 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 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
Published2021
Admission routes2
Has abstractyes

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