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Record W3009470277 · doi:10.25071/1916-4467.40430

Book Review: Pedagogies of Re-Imagination and Unlearning: Decolonial Cracks Within/Against Settler Colonial Canada

2020· article· en· W3009470277 on OpenAlexaffvenueabout
Brooke Charlebois, Megan Ewing, A. M. Davies, M. Rajavel, Adam Wrestch, Heather Sykes

Bibliographic record

VenueJournal of the Canadian Association for Curriculum Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsColonialismDecolonialityIdentity (music)SociologyEmpireReading (process)CurriculumAestheticsPolitical scienceArtLawPedagogy

Abstract

fetched live from OpenAlex

This book review is a close reading of three book-length works by key, contemporary scholars in the field of settler colonial studies: Walter Mignolo and Catherine Walsh's On Decoloniality; Adam Dahl's The Empire of the People; and Emma Battell Lowman and Adam Barker's Settler: Identity and Colonialism in 21st Century Canada. This review provides a critical account of the significance of navigating the complexities of modern settler colonial practices and frameworks within Western settler societies to better inform and navigate our own decolonizing processes. We identify settler logics, perspectives and foundational frameworks as key factors in our current educative practices. Through this, we debate the significance of unsettling our/selves to consider extensions of our identities through a decolonial lens and how we, as a society, contribute to ongoing colonial processes. The review also provides approaches to how these resources may be used to deepen our anti-colonial lens by considering these texts as an underlying basis to reflect upon current educative curricula.

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)0.005
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: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.787
Threshold uncertainty score0.424

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.009
Science and technology studies0.0020.002
Scholarly communication0.0040.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0170.003

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 head0.019
GPT teacher head0.318
Teacher spread0.299 · 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
GenreReview

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
Published2020
Admission routes3
Has abstractyes

Explore more

Same venueJournal of the Canadian Association for Curriculum StudiesSame topicIndigenous Health, Education, and RightsFrench-language works237,207