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Record W1833380749 · doi:10.18357/ijih51200912329

Resilience and Aboriginal Communities in Crisis: Theory and Interventions

2009· article· en· W1833380749 on OpenAlexaffvenue
Michel Tousignant, Nibisha Sioui

Bibliographic record

VenueInternational Journal of Indigenous Health · 2009
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsSociologyEnvironmental ethicsCoping (psychology)Social capitalCommunity cohesionPsychological interventionEthosPsychological resilienceSocial psychologyPublic relationsPsychologyPolitical scienceLawSocial sciencePsychotherapist

Abstract

fetched live from OpenAlex

Resilience in Aboriginal communities is a long process of healing that allows to supersede the multiple trauma and the loss of culture experienced during the colonization and after. The presence of social capital is central to this process in building bridges between persons, families and social groups with the aim of developing a spirit of civic culture. The process usually relies in the first stage on the vision of a few leaders whose example brings forward a larger segment of their community. Characteristics specific to the notion of resilience in Aboriginal cultures are: spirituality, holism, resistance and forgiveness. The main obstacle to overcome in the process of resilience is the phenomenon of codependency which leads to superficial attachment, lack of trust, and refusal of authority. The concept of cultural identity is central to resilience in this context and there is a need to create a new cultural ethos in continuity with the traditions. Each community has to undergo its own course and cannot copy success stories, mainly be inspired through a process of lateral knowledge transfer. Finally, community resilience has to rely on the capacity of families to be resilient themselves which involves breaking the law of silence, naming problems and coping with them with the support of networks and institutions.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.026
GPT teacher head0.463
Teacher spread0.436 · 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 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

Citations74
Published2009
Admission routes2
Has abstractyes

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