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Record W4319081469 · doi:10.3390/genealogy7010009

“Community Envelops Us in This Grey Landscape of Obstacles and Allows Space for Healing”: The Perspectives of Indigenous Youth on Well-Being

2023· article· en· W4319081469 on OpenAlexaffabout
Johnny Boivin, Marie-Hélène Canapé, Sébastien Lamarre-Tellier, Alicia Ibarra-Lemay, Natasha Blanchet‐Cohen

Bibliographic record

VenueGenealogy · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsConcordia University
Fundersnot available
KeywordsIndigenousParticipatory action researchConversationSolidarityReflexivityAgency (philosophy)Citizen journalismPublic relationsSpace (punctuation)SociologyWitnessPolitical scienceSocial scienceCommunicationLawAnthropology

Abstract

fetched live from OpenAlex

This paper presents Indigenous youths’ perspectives on well-being. Using Indigenous youth participatory action research with the Indigenous youth advisory committee of the Québec Youth Research Network Chair (Canada), community care emerged as the central feature of well-being and was then visually presented in the form of a postcard. We discuss the meaning given to community care, the factors that support it, and the role that a visual illustration can play in promoting change. The article is informed by the co-creation of the postcard, an online luncheon conversation, and several debriefing/reflexive sessions with the Indigenous youth co-authors. Emphasis is placed on cultural continuity, relational agency, and solidarity, offering an alternative point of view to the prevalent and damaging decontextualized, deficit-based, and individualized approaches to well-being.

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)0.006
metaresearch head score (Gemma)0.004
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 score0.156
Threshold uncertainty score0.311

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0210.038
Scholarly communication0.0070.004
Open science0.0010.008
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.303
Teacher spread0.279 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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