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Record W2911764592 · doi:10.18584/iipj.2019.10.1.3

Success in Closing the Socio-Economic Gap, But Still a Long Way to Go: Urban Aboriginal Disadvantage, Trauma, and Racism in the Australian City of Newcastle

2019· article· en· W2911764592 on OpenAlexvenueno aff
Deirdre Howard‐Wagner

Bibliographic record

VenueInternational Indigenous Policy Journal · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
FundersAustralian Research Council
KeywordsDisadvantageIndigenousRacismMainstreamSociologyGender studiesIndividualismPolitical scienceLaw

Abstract

fetched live from OpenAlex

The research presented in this article is based on a four-year place-based qualitative case study of Aboriginal success in addressing Aboriginal disadvantage in the Australian city of Newcastle. The article presents extracts from in-depth interviews with Aboriginal people working on a day-to-day basis with Aboriginal and/or Torres Strait Islander people experiencing disadvantage in this city. Interviewees define Indigenous disadvantage in a way that differs considerably from how it is defined in mainstream policy circles. They describe Indigenous disadvantage as being grounded in the histories of social exclusion from Australian society, rather than merely a contemporary phenomenon related socio-economic factors (i.e., lack of educational and employment opportunities). They indicated that it was (a) closely tied to Aboriginal experiences of displacement and trauma; (b) not just a material problem but a historical and social structural problem; and (c) unique to each community. For instance, urban Indigenous disadvantage is distinct from Indigenous disadvantage in remote areas. This supports the claims of Indigenous sociologist Maggie Walter (2009). In doing so, the article more strongly aligns with a critique of a neo-liberal racial project, which defines Indigenous disadvantage within an individualistic framework of individual rights and in terms of socio-economic gaps, from the voices of Aboriginal representatives.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.680
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.391
Teacher spread0.363 · 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.

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

Citations8
Published2019
Admission routes1
Has abstractyes

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