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Record W2405720382 · doi:10.7870/cjcmh-2016-028

Going Off, Growing Strong: Building Resilience of Indigenous Youth

2016· article· en· W2405720382 on OpenAlexaffvenueabout
Christina Hackett, Christopher Furgal, Dorothy Angnatok, Tom Sheldon, S. Karpik, D. Baikie, Carla Pamak, Tessa Bell

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2016
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsMemorial University of NewfoundlandTrent UniversityMcMaster UniversityGovernment of Nunavut
Fundersnot available
KeywordsResilience (materials science)IndigenousPositive Youth DevelopmentYouth studiesPsychological resiliencePsychologySociologySocial psychologyDevelopmental psychologyEcology

Abstract

fetched live from OpenAlex

Going Off, Growing Strong is a program for Inuit youth facing widespread social, cultural, and economic change. The overarching goals of the program are to: (1) enhance resilience and wellness; (2) build social connections for the youth; and (3) transmit traditional knowledge, skills, and values to participating youth.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.966
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0080.002
Scholarly communication0.0020.002
Open science0.0010.008
Research integrity0.0010.003
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 head0.071
GPT teacher head0.386
Teacher spread0.314 · 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 designObservational
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

Citations18
Published2016
Admission routes3
Has abstractyes

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