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Record W3130260060 · doi:10.32799/ijih.v16i2.31668

Project George: An Indigenous Land-Based Approach to Resilience for Youth

2020· article· en· W3130260060 on OpenAlexaffvenueabout
Janice Cindy Gaudet

Bibliographic record

VenueInternational Journal of Indigenous Health · 2020
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIndigenousPsychological resilienceCommunity engagementContext (archaeology)HomelandParticipatory action researchSociologyEcological resilienceEnvironmental resource managementPolitical scienceGeographyPublic relationsEcologyPsychologyPoliticsAnthropologyArchaeologySocial psychology

Abstract

fetched live from OpenAlex

The research shared in this article seeks an understanding of Indigenous resilience within the context of a culturally responsive land-based initiative, Project George, led by the Moose Cree First Nation, also known as the Omushkego people. The initiative centres core Cree values, community engagement, and land-based skills to ensure the well-being of youth. Their Homeland is located in the waterways and on the western shores of the Hudson and James Bay Lowlands in Ontario, Canada. The methodology involved researcher participation and engagement as part of a 4-month field presence; informal conversations and visiting; as well as formal semistructured interviews with community members over 4 years from 2012 to 2015. The research explores the benefits and challenges of a land-based program by highlighting the experiences and voices of community and program participants who directly engaged with Project George. The findings show that land-based learning initiatives inspired and driven by Indigenous people foster a regenerative approach to wellness based on relation to land, culture, and identity. A return to land-based learning responds to the ongoing colonial complexities affecting the health and wellness of Indigenous youth in Canada and draws strength from the people’s resilient practices.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.661

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.075
GPT teacher head0.444
Teacher spread0.369 · 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.

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

Citations9
Published2020
Admission routes3
Has abstractyes

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