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Record W1973455243 · doi:10.1080/13691058.2010.520742

Learning from the past: young Indigenous people's accounts of blood-borne viral and sexually transmitted infections as resilience narratives

2010· article· en· W1973455243 on OpenAlexfundno aff
Julie Mooney‐Somers, Anna Olsen, Wani Erick, R. Douglas Scott, Angie Akee, John Kaldor, Lisa Maher

Bibliographic record

VenueCulture Health & Sexuality · 2010
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsnot available
FundersHealth Research Council of New ZealandNational Health and Medical Research CouncilCanadian Institutes of Health ResearchMedical Research CouncilAustralian Government
KeywordsNarrativeContext (archaeology)IndigenousIgnorancePsychological resilienceAgency (philosophy)Narrative inquiryNormativeSociologyPsychologySocial psychologyPolitical scienceGeographySocial scienceEcologyBiology

Abstract

fetched live from OpenAlex

The Indigenous Resilience Project is an Australian community-based participatory research project using qualitative methods to explore young Aboriginal and Torres Strait Islander people's views of blood-borne viral and sexually transmitted infections (BBV/STI) affecting their communities. In this paper we present an analysis of narratives from young people who had a previous BBV/STI diagnosis to explore how they actively negotiate the experience of BBV/STI infection to construct a classic resilience narrative. We examine two overarching themes: first, the context of infection and diagnosis, including ignorance of STI/BBV prior to infection/diagnosis and, second, turning points and transformations in the form of insights, behaviours, roles and agency. Responding to critical writing on resilience theory, we argue that providing situated accounts of adversity from the perspectives of young Indigenous people prioritises their subjective understandings and challenges normative definitions of resilience.

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)0.009
metaresearch head score (Gemma)0.014
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 score0.052
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0170.027
Scholarly communication0.0080.009
Open science0.0020.012
Research integrity0.0020.004
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 head0.016
GPT teacher head0.368
Teacher spread0.352 · 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

Citations25
Published2010
Admission routes1
Has abstractyes

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