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Record W2750613439 · doi:10.1080/10455752.2017.1368680

Disrupting the Settler Colonial University: Decolonial Praxis and Place-Based Education in the Okanagan Valley (British Columbia)

2017· article· en· W2750613439 on OpenAlexaffabout
Levi Gahman, Gabrielle Legault

Bibliographic record

VenueCapitalism Nature Socialism · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsColonialismPraxisDecolonizationIndigenousSociologyLegitimacyMainstreamStatus quoGender studiesPolitical scienceLawPolitics

Abstract

fetched live from OpenAlex

This article demonstrates how decolonial Placed-Based Education can disrupt a settler colonial academic status quo. We begin by situating our analysis in the unceded Syilx Territories of the Okanagan Valley (British Columbia, Canada) and proceed by illustrating how both taken-for-granted colonial epistemologies and banal exnominations of white supremacy remain orthodox within mainstream Canadian higher education. We next define “decolonial praxis” by drawing from insights offered by critical feminist, anti-racist, and Indigenous scholars and community organizers before moving into a summary of how we embraced theories and strategies of decolonization coupled with Place-Based Education in an introductory Gender and Women’s Studies course. We conclude with our response to the ongoing exclusions being reproduced by neoliberal universities that result from the primacy they grant to Western knowledges and rationales. The piece reveals how decolonial place-based methods can be leveraged against settler colonial institutions, discourses, and logics to unsettle their claims to legitimacy, land, and authority over learning.

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)0.002
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 score0.059
Threshold uncertainty score0.425

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0260.023
Scholarly communication0.0070.002
Open science0.0010.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.007
GPT teacher head0.284
Teacher spread0.277 · 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

Citations18
Published2017
Admission routes2
Has abstractyes

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