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Record W4220731805 · doi:10.1080/15595692.2022.2055542

Exploring self-determined urban Indigenous adult education in an Indigenous organization

2022· article· en· W4220731805 on OpenAlexafffundabout
Angela Easby, Aleksandra Bergier, Kim Anderson

Bibliographic record

VenueDiaspora Indigenous and Minority Education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsQueen's UniversityUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousTraditional knowledgeKnowledge transferSociologyIndigenous educationPublic relationsPolitical scienceGeographyKnowledge managementEcology

Abstract

fetched live from OpenAlex

Urban Indigenous communities in Canada are sites of dynamic knowledge transfer among Indigenous people who build community together both from within similar cultural frameworks and across difference. These “inter-national” urban Indigenous communities face distinct challenges and opportunities for implementing Indigenous knowledge transfer processes. This article examines the mechanisms through which knowledge transfer occurs at the Ontario Federation of Indigenous Friendship Centres, a large urban Indigenous organization in Toronto, Ontario. The researchers used interviews and focus groups to explore strategies for knowledge transfer among Elders, Knowledge Keepers, leaders, and staff. We argue that urban Indigenous communities transfer knowledge through processes that are sensitized to the diverse inter-national nature of these environments while at the same time oriented toward achieving the continuity of a common knowledge base. Thinking about these processes and their underlying goals through an educational lens helps broaden understandings of where Indigenous education occurs to include professional workplaces.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0130.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.283
Teacher spread0.264 · 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 teacher head, not a consensus.

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

Citations0
Published2022
Admission routes3
Has abstractyes

Explore more

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