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Record W3003837299 · doi:10.1080/23268743.2019.1695538

NDNGirls and Pocahotties: Native American and First Nations representation in settler colonial pornography and erotica

2020· article· en· W3003837299 on OpenAlexaboutno aff
James Mackay

Bibliographic record

VenuePorn Studies · 2020
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousColonialismPornographyHuman sexualityGender studiesNative americanContext (archaeology)Representation (politics)HistorySociologyEthnologyPolitical sciencePoliticsLawArchaeology

Abstract

fetched live from OpenAlex

This article argues that Indigenous sexualities in settler colonial states are regulated by an eliminationist logic that requires either the elimination or subjugation of the Native body in pornography. The authors first explore the functioning of such a mechanism in the fetishized and eroticized ‘Pocahottie’ costumes worn by non-Native women, placing this in a wider context of the treatment of Indigenous women in settler colonial North America. We then examine the history of Native American characters and scenarios in hardcore American pornography, and the lack of Native performers in pornography, noting the ways that actual Native performers are insistently placed in inaccurate racial categories such as Asian and Latina. We then explore the iconography of NDNGirls.com, the only company ever to have been devoted to filming female Indigenous performers. Finally, the article considers ways that Indigenous erotica combats eliminationist thinking.

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)0.001
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 score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.010
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.074
GPT teacher head0.397
Teacher spread0.323 · 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

Citations40
Published2020
Admission routes1
Has abstractyes

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