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Record W2913679136 · doi:10.1177/1177180119828075

The Turtle Lodge: sustainable self-determination in practice

2019· article· en· W2913679136 on OpenAlexafffundabout
Laura Cameron, Dave Courchene, Sabina Ijaz, Ian Mauro

Bibliographic record

VenueAlterNative An International Journal of Indigenous Peoples · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsFirst Nations Health and Social Secretariat of ManitobaUniversity of Winnipeg
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsTurtle (robot)IndigenousSelf-determinationEnvironmental ethicsSociologyScholarshipCorporate governancePolitical scienceSustainabilitySelf-governanceLegal guardianProcess (computing)Environmental resource managementLawEcologyManagement

Abstract

fetched live from OpenAlex

The Turtle Lodge International Centre for Indigenous Education and Wellness in Sagkeeng First Nation, Manitoba, is leading the way in exemplifying and cultivating sustainable self-determination. This is a holistic concept and process that recognizes the central role that land and culture play in self-determination, and the responsibility to pass these teachings on to future generations. This article links theory and practice in the emerging scholarship on sustainable self-determination and examines how Turtle Lodge embodies sustainable self-determination through traditional governance and laws, respectful and reciprocal relationships, cultivation of cultural revitalization and community well-being, and efforts to inspire earth guardianship. Turtle Lodge’s experience underscores the importance of understanding sustainable self-determination as a flexible, community-based process. This case study fits within recent calls in the literature for a shift from a rights-based to responsibility-based self-determination discourse and demonstrates some of the challenges and lessons learned that might support other communities pursuing similar actions.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.002
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 head0.009
GPT teacher head0.338
Teacher spread0.329 · 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

Citations37
Published2019
Admission routes3
Has abstractyes

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