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Record W3108377827 · doi:10.1017/jie.2020.26

Contributions to urban Indigenous self-determination: the story of Neeginan and Kaupapa Māori

2020· article· en· W3108377827 on OpenAlexaffabout
Darwin Horning, Beth Baumbrough

Bibliographic record

VenueThe Australian Journal of Indigenous Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsIndigenousIndigenous educationSociologyContext (archaeology)Traditional knowledgeGovernment (linguistics)Bilingual educationPedagogyGender studiesSocial scienceMedia studiesGeography

Abstract

fetched live from OpenAlex

Abstract This paper considers two different Indigenous-led initiatives, the Neeginan initiative (Winnipeg, Canada) and the Kaupapa Māori movement (New Zealand), within the context of urban Indigenous self-determination, examining the role, or contributions of, each towards the realisation of Indigenous self-determination. Neeginan originates from, and focuses on, building a sense of community, through education programs, social assistance and affordable housing, with local Indigenous knowledge providing the foundational guiding principles. This is compared to the Kaupapa Māori movement's role in the revival of traditional cultural and language practices in education, which has resulted in the development of an overwhelmingly successful parallel non-government school system based on Māori culture, language and philosophy.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.112
Threshold uncertainty score0.224

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0330.034
Scholarly communication0.0060.004
Open science0.0020.010
Research integrity0.0030.007
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 head0.323
Teacher spread0.306 · 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

Citations2
Published2020
Admission routes2
Has abstractyes

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