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Record W2954976133 · doi:10.1080/14927713.2019.1629832

Pathways to Reconciliation: the Kitcisakik Land-Based Education Initiative

2019· article· en· W2954976133 on OpenAlexafffundvenueabout
Alexandra Arellano, Joseph Friis, Stephen A. Stuart

Bibliographic record

VenueLeisure/Loisir · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsSaint Paul UniversityUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsGrassrootsIndigenousExperiential learningExperiential educationSociologyColonialismPlace-based educationDecolonizationGender studiesPolitical scienceEnvironmental ethicsPedagogyLawEnvironmental educationEcology

Abstract

fetched live from OpenAlex

Grounded in settler colonialism and resurgence, this paper presents multiple accounts gleaned from students who underwent experiential ways of learning about Indigenous cultures and sociopolitical issues through a land-based education experience considered as a contribution to building reconciliation. For over 8 years, the semi-nomadic community of Kitcisakik in Western Québec has received non-Indigenous students to experience living ‘off the land’ and share Anicinape ways. Originally inspired by a community desire to share and transmit Anicinape culture while re-appropriating wider territories of their ‘occupied’ ancestral land, this became a grassroots social economy initiative, offering 4- to 10- day educational trips for students. In addition to encouraging cultural dialogue and mutual respect, the initiative exemplifies the benefits of Indigenous learning pedagogies that are both experiential and land-based. Students describe how they physically and spiritually encounter Indigenous resilience via a confrontation of the neocolonial intricacies evident in contemporary Canada. Such critical self-reflection is necessary in Canada’s nascent era of 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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.035
GPT teacher head0.298
Teacher spread0.263 · 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 designNot applicable
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

Citations10
Published2019
Admission routes4
Has abstractyes

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