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Record W2969267965 · doi:10.18584/iipj.2019.10.3.8165

Quantification of Interplaying Relationships Between Wellbeing Priorities of Aboriginal People in Remote Australia

2019· article· en· W2969267965 on OpenAlexvenueno aff
Rosalie Schultz, Stephen Quinn, Tammy Abbott, Sheree Cairney, Jessica Yamaguchi

Bibliographic record

VenueInternational Indigenous Policy Journal · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
FundersVincent Fairfax Family FoundationAustralian GovernmentCharles Darwin UniversityDepartment of the Prime Minister and CabinetFlinders UniversityRoyal Australasian College of Physicians
KeywordsEmpowermentLiteracyGovernment (linguistics)IndigenousSociologyPublic relationsEconomic growthPolitical sciencePedagogyEcology

Abstract

fetched live from OpenAlex

Wellbeing is a useful indicator of social progress because its subjectivity accounts for diverse aspirations. The Interplay research project developed a wellbeing framework for Aboriginal people in remote Australia comprising government and community wellbeing priorities. This article describes statistical modelling of community priorities based on surveys administered by community researchers to 841 participants from four remote settlements. Constructs for Aboriginal language literacy, cultural practice, and empowerment were identified through exploratory factor analysis (EFA); structural equation modeling (SEM) was used to confirm relationships. Cultural practice was associated with Aboriginal language literacy and empowerment, which were both associated with wellbeing. Aboriginal literacy and empowerment mediated negative direct relationships between cultural practice and wellbeing. Direct relationships were significant only for females for whom empowerment and Aboriginal literacy appear key to enhancing wellbeing.

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)0.004
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.003
Research integrity0.0000.001
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 head0.046
GPT teacher head0.401
Teacher spread0.355 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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