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Cultural Appropriation: The Chant of Jimmie Blacksmith , Black Robe and Dance Me Outside

2023· book-chapter· en· W4391119458 on OpenAlexaboutno aff
Gillian Roberts

Bibliographic record

VenueEdinburgh University Press eBooks · 2023
Typebook-chapter
Languageen
FieldArts and Humanities
TopicShakespeare, Adaptation, and Literary Criticism
Canadian institutionsnot available
Fundersnot available
KeywordsWhite (mutation)IndigenousHistoryAppropriationDanceNarrativeBrotherArt historyGenealogyColonialismArtLiteratureAnthropologySociologyPhilosophy

Abstract

fetched live from OpenAlex

This chapter focuses on literary and cinematic texts in which Indigenous peoples have been appropriated by non-Indigenous authors and filmmakers: The Chant of Jimmie Blacksmith (1978), written by Australian author Thomas Keneally and adapted by Australian director Fred Schepisi; Black Robe (1991), written by Northern Irish-Canadian author Brian Moore (1984) and adapted by Australian director Bruce Beresford; and Dance Me Outside (1994), stories written by Canadian author W.P. Kinsella and adapted by Canadian director Bruce McDonald. The chapter probes the ways in which settler-colonial culture borrows from Indigenous cultures. These case studies were produced either prior to or in the midst of debates surrounding the legitimacy of non-Indigenous representations of Indigenous culture in the late 1980s and early 1990s. The chapter also discusses multiple adaptations through historical antecedents to the novels (Keneally’s novel’s basis in the historical figure of Jimmy Governor, a mixed-race man who, along with his brother, murdered several white people in early twentieth-century Australia; Black Robe’s use of the Jesuit writings in New France) and the continuation of Dance Me Outside’s narrative and characters in Nick Craine’s graphic-novel version of the screenplay ultimately used to shoot the film (1994) and CBC television’s The Rez (1995-1997).

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0230.015
Scholarly communication0.0070.004
Open science0.0010.004
Research integrity0.0010.004
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 head0.052
GPT teacher head0.206
Teacher spread0.154 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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