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Record W2167113258 · doi:10.7202/040778ar

Aboriginal-Social Justice Alliances: Understanding the Landscape of Relationships through the Coalition for a Public Inquiry into Ipperwash

2007· article· en· W2167113258 on OpenAlexvenueaboutno aff
Lynne Davis, Vivian O'Donnell, Heather Shpuniarsky

Bibliographic record

VenueInternational Journal of Canadian Studies · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
FundersUniversity of CambridgeUniversity of ChicagoYale University
KeywordsIndigenousContext (archaeology)Economic JusticeSociologyShadow (psychology)Environmental justiceNarrativeGrounded theoryResource (disambiguation)Political sciencePublic relationsSocial scienceQualitative researchGeographyLawEcologyPsychology

Abstract

fetched live from OpenAlex

Despite their growing popularity, alliances and coalitions between Indigenous peoples and other actors fighting for social and environmental justice have been little documented or analyzed. Alliances form often in the context of land and resource disputes, struggles against discrimination and racism, and other areas of life where there are grounds for strategic co-operation. Using grounded theory and resource mobilization theory, this study examines relationships between social justice and Indigenous activists who formed the "Coalition for a Public Inquiry into Ipperwash", a social justice struggle in Ontario, Canada. The authors analyze participants' narratives noting their understandings of their relationships, strengths and tensions, and lessons learned. It is apparent that Indigenous and social movement alliances represent an exceptional site of encounter and transformation, always in the shadow of ongoing colonization and the movement to Indigenous self-determination. The Coalition provides a window into complex relationships that are forming across Canada and globally.

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)0.008
metaresearch head score (Gemma)0.010
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 score0.313
Threshold uncertainty score0.629

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0360.044
Scholarly communication0.0110.006
Open science0.0020.012
Research integrity0.0020.004
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 head0.195
GPT teacher head0.430
Teacher spread0.235 · 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

Citations4
Published2007
Admission routes2
Has abstractyes

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Same venueInternational Journal of Canadian StudiesSame topicIndigenous Health, Education, and RightsFrench-language works237,207