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Record W2982381054 · doi:10.1080/15595692.2019.1669015

Paddling as resistance? Exploring an Indigenous approach to land-based education amongst Manitoba youth

2019· article· en· W2982381054 on OpenAlexaffabout
Jay Johnson, Adam Ehsan Ali

Bibliographic record

VenueDiaspora Indigenous and Minority Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsQueen's UniversityUniversity of Manitoba
Fundersnot available
KeywordsIndigenousTransformative learningResistance (ecology)AdventureColonialismCeremonyIdentity (music)SociologyEthnographyDecolonizationGender studiesGeographyPolitical sciencePedagogyAnthropologyArchaeologyEcologyHistoryAestheticsArt

Abstract

fetched live from OpenAlex

A group involving Métis and Indigenous graduate students from the University of Manitoba and inner-city Indigenous youth developed and participated in an outdoor adventure-based canoe trip in Quetico Provincial Park. The five-day trip was steeped in Métis, Voyageur and Indigenous history and ceremony. This ensuing paper focuses on what is at stake when taking a decolonizing approach toward land-based education involving both Indigenous and non-Indigenous participants that aspires to (re)introduce Indigenous practices, environments and ceremonies. Inspired by recognizing the complexities involved in creating physical activity and sport programming for Indigenous youth under colonial structures within and outside of the academy, we seek to both illuminate and deconstruct the possibilities of measuring the transformative effects that such a trip has on all those involved, including the youth, mentors, and researchers. Drawing on the results of a qualitative study using both Indigenous and Western approaches, we present community-based research focusing on the impacts these experiences have on community building, identity, and decolonization.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.675
Threshold uncertainty score0.646

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.009
Scholarly communication0.0050.002
Open science0.0020.007
Research integrity0.0010.002
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 head0.028
GPT teacher head0.286
Teacher spread0.258 · 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

Citations14
Published2019
Admission routes2
Has abstractyes

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