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Record W2766701568

Unpacking the micro-macro nexus: narratives of suffering and hope among refugees from Burma recently settled in Australia

2013· article· en· W2766701568 on OpenAlexaff
Mark Brough, Robert Schweitzer, Jane Shakespeare‐Finch, Lyn Vromans, Julie King

Bibliographic record

VenueQUT ePrints (Queensland University of Technology) · 2013
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsVictoria Park
Fundersnot available
KeywordsRefugeeNarrativeContext (archaeology)PoliticsGender studiesSociologyNexus (standard)Face (sociological concept)Psychological resiliencePolitical scienceSocial psychologyPsychologyHistorySocial scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

Narratives of forced migration are open to a variety of interpretations. In mental health, refugee narratives of arduous journeys in the face of systemic macro socio-political forces are often transformed from this context into a medicalized micro context of inner individual worlds. Both the dominant pathogenic lens of trauma studies and the growing salutogenic lens embodied in resilience research, often reflect a western cultural idiom of focusing on the individualized nature of these phenomena. Using qualitative data collected from refugees from Burma now settling in Australia, the article emphasizes the need for a more reflexive and expansive account of both suffering and hope within refugee narratives. It recounts these narratives within a conceptual framework which acknowledges the importance of the connections between the micro individual experience and the macro, socio-political context. This is not only a question of political principle, but also a matter of listening to the voice of those who know most about the relationship between macro forces of human rights violations and their impact on individual, family and community trajectories.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.260
Teacher spread0.247 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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