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Social Justice Activism in the Heartland of Hate: Countering Extremism in Alberta

2006· article· en· W286304240 on OpenAlexafffundvenueabout
Darren E. Lund

Bibliographic record

VenueAlberta Journal of Educational Research · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsUniversity of Calgary
FundersSocial Sciences and Humanities Research Council of CanadaKillam Trusts
KeywordsSocial justiceCriminologyEconomic JusticeViolent extremismHate crimeRacial biasPolitical scienceSociologyRacismLawTerrorism

Abstract

fetched live from OpenAlex

This article addresses Alberta’s conservative political and social milieu with attention to teachers engaged with their students in school activism on social justice issues. Its purpose is to shed light on the experiences of teachers who address extremism through school-based activism with young people. A brief historical overview of Canada’s racist past includes a focus on Alberta’s specific regional political scene and on hate-group activities over the past several decades. The effect is traced of this past on contemporary discourses about diversity. Examples of responses to diversity backlash and extremism are offered with reference to a particular student social justice program and current research that studies diversity activism in Canadian schools. Excerpts from interviews with teacher activists address how their work is affected by this context as they implement social justice initiatives in schools.

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 score0.057
Threshold uncertainty score0.417

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0460.013
Scholarly communication0.0060.001
Open science0.0020.004
Research integrity0.0020.003
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 head0.063
GPT teacher head0.396
Teacher spread0.334 · 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
Published2006
Admission routes4
Has abstractyes

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