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Record W7132869068

Looking in the mirror, looking in the curriculum: the perpetuation of Eurocentric images

2007· dissertation· W7132869068 on OpenAlexaboutno aff
Janine Elizabeth Brown

Bibliographic record

VenueTSpace · 2007
Typedissertation
Language
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsMindsetAppropriationResistance (ecology)White (mutation)ColonialismPolitics
DOInot available

Abstract

fetched live from OpenAlex

This thesis will explore, through a lens of sociology and education, the ongoing results of the attempted destruction of Native peoples of North America through assimilation and genocide. It will examine the impact of white appropriation of Native imagery in the residential school system and the current Ontario curriculum. Using an anti-racist approach this thesis will investigate the continuing presence of colonial mindset in our society, pointing out the problems of not creating a holistic learning environment where students are learners, and teachers act as guides. It will be argued that resistance to Eurocentric images involve work on many fronts, from analysis of cultural, political and economic practices, globally and locally, to active work in classroom settings. Positive images put forward by Native activists, authors, storytellers and educators, thus demonstrating the re-emergence of traditional leadership patterns among Aboriginals, will contribute to the rebuilding of Native people's sense of autonomy.

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)0.004
metaresearch head score (Gemma)0.006
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 score0.020
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.035
Scholarly communication0.0070.004
Open science0.0010.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.382
Teacher spread0.365 · 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
Published2007
Admission routes1
Has abstractyes

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