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Record W3042045367 · doi:10.18357/ijcyfs113202019696

ȻENTOL TŦE TEṈEW (TOGETHER WITH THE LAND)

2020· article· en· W3042045367 on OpenAlexafffundvenue
Morgan Mowatt, Sandrina de Finney, Sarah Wright Cardinal, Jilleun Tenning, Pawa Haiyupis, Erynne M. Gilpin, Dorothea Harris, Ana Celeste MacLeod, Nick Claxton

Bibliographic record

VenueInternational Journal of Child Youth and Family Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsUniversity of Victoria
FundersMinistry of Advanced Education
KeywordsIndigenousIndigenous educationStorytellingSociologyTraditional knowledgeFace (sociological concept)PedagogyPolitical sciencePublic relationsSocial scienceNarrativeEcology

Abstract

fetched live from OpenAlex

This article presents reflections from an Indigenous land- and water-based institute held from 2019 to 2020 for Indigenous graduate students. The institute was coordinated by faculty in the School of Child and Youth Care at the University of Victoria and facilitated by knowledge keepers in local W̱SÁNEĆ and T’Sou-ke nation territories. The year-long institute provided land-based learning, sharing circles, online communication, and editorial mentoring in response to a lack of Indigenous pedagogies and the underrepresentation of Indigenous graduate students in frontline postsecondary programs. While Indigenous faculty and students continue to face significant, institutionally entrenched barriers to postsecondary education, we also face growing demands for Indigenous-focused learning, research, and practice. In this article, Part 1 of a two-paper series on Indigenous land- and water-based learning and practice, we draw on a storytelling approach to share our individual and collective reflections on the benefits and limitations of Indigenous land- and water-based pedagogies. Our stories and analysis amplify our integration of Indigenous ways of being and learning, with a focus on local knowledges and more ethical land and community engagements as integral to Indigenous post­secondary education.

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)0.000
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 score0.415
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.038
GPT teacher head0.303
Teacher spread0.265 · 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

Citations3
Published2020
Admission routes3
Has abstractyes

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