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Record W4360810557 · doi:10.1093/heapro/daac190

First Nations music as a determinant of health in Australia and Vanuatu: political and economic determinants

2023· article· en· W4360810557 on OpenAlexaboutno aff
Naomi Sunderland, Phil Graham, Brydie‐Leigh Bartleet, Darren Garvey, Clint Bracknell, Kristy Apps, Glenn Barry, Rae Cooper, Brigitta Scarfe, Stacey Vervoort

Bibliographic record

VenueHealth Promotion International · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsPolitical scienceGeography

Abstract

fetched live from OpenAlex

This article reports on findings that indicate how First Nations musical activities function as cultural determinants of health. Drawing on early findings from a 3-year Australian Research Council funded project titled The Remedy Project: First Nations Music as a Determinant of Health, we detail Australian and Ni Vanuatu First Nations musicians' reported outcomes of musical activity using a First Nations cultural determinants of health framework. The broader findings indicate that our respondents see musical activity as actively shaping all known domains of cultural health determinants, and some surrounding political and social determinants. However, this paper focusses specifically on the political and economic determinants that emerged in analysis as the most dominant subthemes. We argue that this study provides strong impetus for continued investigation and reconceptualization of the place of music in cultural health determinant models.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.062
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.002
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 head0.077
GPT teacher head0.425
Teacher spread0.348 · 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

Citations13
Published2023
Admission routes1
Has abstractyes

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