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Record W2127197891 · doi:10.1111/cura.12058

My Sisters Will Not Speak: Boas, Hunt, and the Ethnographic Silencing of First Nations Women

2014· article· en· W2127197891 on OpenAlexaboutno aff
Margaret Bruchac

Bibliographic record

VenueCurator The Museum Journal · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousEthnographyGeorge (robot)SociologyGender studiesAnthropologyHistoryArt history

Abstract

fetched live from OpenAlex

Abstract First Nations women were instrumental to the collecting of Northwest Coast Indigenous culture, yet their voices are nearly invisible in the published record. The contributions of George Hunt, the Tlingit/British culture broker who collaborated with anthropologist Franz Boas, overshadow the intellectual influence of his mother, Anislaga Mary Ebbetts, his sisters, and particularly his Kwakwaka'wakw wives, Lucy Homikanis and Tsukwani Francine ‘Nakwaxda'xw. In his correspondence with Boas, Hunt admitted his dependence upon high‐status Indigenous women, and he gave his female relatives visual prominence in film, photographs, and staged performances, but their voices are largely absent from anthropological texts. Hunt faced many unexpected challenges (disease, death, arrest, financial hardship, and the suspicions of his neighbors), yet he consistently placed Boas' demands, perspectives, and editorial choices foremost. The resulting cultural representations marginalized the influence of the First Nations women who had been integral to their creation.

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)0.007
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.974
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0260.022
Scholarly communication0.0080.004
Open science0.0010.005
Research integrity0.0020.003
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 head0.023
GPT teacher head0.285
Teacher spread0.263 · 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.

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

Citations11
Published2014
Admission routes1
Has abstractyes

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