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Record W7162079726 · doi:10.82308/5861

Seven spans thick: exploring resilience from the perspectives of Aboriginal peoples living off-reserve

2009· dissertation· en· W7162079726 on OpenAlexaboutno aff
Charlotte Hardie

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsResilience (materials science)Capital (architecture)Psychological resiliencePerspective (graphical)

Abstract

fetched live from OpenAlex

Puisque la plupart des études sur les questions autochtones se concentrent sur les problèmes et les conflits, on oublie souvent d’examiner la force caractère et la persévérance démontrées par les peuples autochtones. Le sujet de cette recherche est le bien-être et la réussite des personnes autochtones qui habitent hors-réserve. Les entrevues ont été faites avec cinq individus autochtones dans une petite communauté au Québec. Les thèmes qui en ressortent démontrent l’importance des liens avec la famille, la culture, la communauté, la terre, et les services sociaux, ainsi que l’importance d’avoir une ‘raison d’être’. La théorie de résilience est à la fois soutenue et élargie par ces résultats, qui se rapportent aussi aux théories sociologiques comme le capital social. Cette étude démontre la valeur d’une approche holistique au bien-être, et souligne l’équilibre de la responsabilité entre l’individu et l’état. Elle souligne aussi l’importance qu’il faut porter aux négotiations entre les différentes cultures.

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)0.006
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 score0.805
Threshold uncertainty score0.388

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0170.011
Scholarly communication0.0060.006
Open science0.0010.008
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.021
GPT teacher head0.334
Teacher spread0.313 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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