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Record W1986681886 · doi:10.1177/1363461514547120

Arctic indigenous youth resilience and vulnerability: Comparative analysis of adolescent experiences across five circumpolar communities

2014· article· en· W1986681886 on OpenAlexfundno aff
Olga Ulturgasheva, Stacy Rasmus, Lisa Wexler, Kristine Nystad, Michael J. Kral

Bibliographic record

VenueTranscultural Psychiatry · 2014
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsnot available
FundersOffice of Polar ProgramsUniversity of Illinois at Urbana-ChampaignUniversity of TorontoNational Science Foundation
KeywordsCircumpolar starArcticIndigenousResilience (materials science)Vulnerability (computing)GeographyPsychological resilienceThe arcticPsychologySociologyOceanographySocial psychologyEcologyComputer security

Abstract

fetched live from OpenAlex

Arctic peoples today find themselves on the front line of rapid environmental change brought about by globalizing forces, shifting climates, and destabilizing physical conditions. The weather is not the only thing undergoing rapid change here. Social climates are intrinsically connected to physical climates, and changes within each have profound effects on the daily life, health, and well-being of circumpolar indigenous peoples. This paper describes a collaborative effort between university researchers and community members from five indigenous communities in the circumpolar north aimed at comparing the experiences of indigenous Arctic youth in order to come up with a shared model of indigenous youth resilience. The discussion introduces a sliding scale model that emerged from the comparative data analysis. It illustrates how a "sliding scale" of resilience captures the inherent dynamism of youth strategies for "doing well" and what forces represent positive and negative influences that slide towards either personal and communal resilience or vulnerability. The model of the sliding scale is designed to reflect the contingency and interdependence of resilience and vulnerability and their fluctuations between lowest and highest points based on timing, local situation, larger context, and meaning.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.173
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.046
GPT teacher head0.391
Teacher spread0.345 · 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.

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

Citations56
Published2014
Admission routes1
Has abstractyes

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