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Record W3202088883 · doi:10.7202/1077729ar

La réconciliation à travers les séjours d’immersion à Kitcisakik

2021· article· fr· W3202088883 on OpenAlexaffvenueabout
Joseph Friis, Alexandra Arellano

Bibliographic record

VenueLes Cahiers du CIÉRA · 2021
Typearticle
Languagefr
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHumanitiesArtPolitical science

Abstract

fetched live from OpenAlex

Depuis 2010, les Anicinapek de Kitcisakik ont mis sur pied une initiative de séjours de sensibilisation, où des voyages éducatifs et d’immersion se sont développés, principalement avec des groupes scolaires et universitaires allochtones. Ces séjours sont devenus des occasions privilégiées de dialogue qui favorisent non seulement la transmission intergénérationnelle et la distinction épistémologique du savoir local, mais aussi la compréhension de la situation sociohistorique et coloniale des peuples autochtones. Rejetant assidument le système de réserve et occupant leur terre ancestrale sans réelles assises juridiques à la lumière du droit positif canadien, les Anicinapek invitent des Allochtones à célébrer leur culture en s’imprégnant du lien à la terre, établissant ainsi la centralité des luttes territoriales dans les processus de réconciliation. Ancrée dans une approche théorique du « colonialisme de peuplement » (« settler-colonialism »), cette réflexion explore le cas particulier de Kitcisakik dans le contexte des séjours d’immersion, invitant les étudiants allochtones à partager leur expérience de sensibilisation. À travers un récit situant les luttes territoriales, le regard des Anicinapek et des étudiants allochtones est mis de l’avant. Les séjours d’immersion offerts par la communauté de Kitcisakik incarnent une ouverture à l’Autre qui offre une expérience privilégiée de réconciliation autocritique.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient 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 score0.879
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0080.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.014
GPT teacher head0.266
Teacher spread0.252 · 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

Citations2
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueLes Cahiers du CIÉRASame topicIndigenous Health, Education, and RightsFrench-language works237,207