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Record W4200599228 · doi:10.1177/17579759211062261

Waiora: the importance of Indigenous worldviews and spirituality to inspire and inform Planetary Health Promotion in the Anthropocene

2021· article· en· W4200599228 on OpenAlexaff
Sione Tu’itahi, Huti Watson, Richard Egan, Margot W. Parkes, Trevor Hancock

Bibliographic record

VenueGlobal Health Promotion · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of VictoriaUniversity of Northern British Columbia
Fundersnot available
KeywordsIndigenousAnthropoceneHealth promotionEnvironmental ethicsSpiritualityPolitical scienceSociologyPublic relationsMedicineLawHealth careEcologyAlternative medicine

Abstract

fetched live from OpenAlex

We now live in a new geological age, the Anthropocene - the age of humans - the start of which coincides with the founding of the International Union for Health Promotion and Education (IUHPE) 70 years ago. In this article, we address the fundamental challenge facing health promotion in its next 70 years, which takes us almost to 2100: how do we achieve planetary health? We begin with a brief overview of the massive and rapid global ecological changes we face, the social, economic and technological driving forces behind those changes, and their health implications. At the heart of these driving forces lie a set of core values that are incompatible with planetary health. Central to our argument is the need for a new set of values, which heed and privilege the wisdom of Indigenous worldviews, as well as a renewed sense of spirituality that can re-establish a reverence for nature. We propose an Indigenous-informed framing to inspire and inform what we call planetary health promotion so that, as the United Nations Secretary General wrote recently, we can make peace with nature.

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)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.018
Scholarly communication0.0080.008
Open science0.0010.006
Research integrity0.0020.008
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 head0.081
GPT teacher head0.392
Teacher spread0.311 · 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 designNot applicable
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

Citations39
Published2021
Admission routes1
Has abstractyes

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