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Record W4319302143 · doi:10.1016/s2542-5196(22)00334-5

On the possibility of decolonising planetary health: exploring new geographies for collaboration

2023· review· en· W4319302143 on OpenAlexafffundabout
Dawn Hoogeveen, Clifford G Atleo, Lyana Patrick, Angel Kennedy, Maëve Leduc, Margot W. Parkes, Tim K. Takaro, Maya Gislason

Bibliographic record

VenueThe Lancet Planetary Health · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of Northern British ColumbiaSimon Fraser University
FundersCanadian Institutes of Health Research
KeywordsIndigenousColonialismScholarshipPublic healthSociologyDiversity (politics)Political sciencePublic relationsMedicineEcologyAnthropology

Abstract

fetched live from OpenAlex

Decolonial planetary health aspires to centre the diversity and importance of Indigenous thought and stewardship. In this Viewpoint, we explore research in planetary health across holistic worldviews and western scientific approaches. We base our examination of decolonising interventions in planetary health by exploring how global trajectories play out in British Columbia, Canada. A central part of this analysis is highlighting intercultural thinking to promote an anti-colonial, anti-racist, and reciprocal approach to climate change and global health inequities across geographical space and within planetary health discourse. Our perspective encompasses an asset-based examination, which focuses on the Indigenous scholarship in planetary health that is already underway and considers how rigorous engagement with epistemic and geographical diversity can strengthen and advance planetary health. This is a place-based response to planetary health, as British Columbia experiences climate catastrophes that are impacting whole communities, cutting through major transportation systems, disrupting supply chains, and creating a further burden on public health agencies and authorities that are spread thin by COVID-19 response. We argue for a progressive acknowledgment of decolonising work that is pushing research and practice in planetary health forward.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.607
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
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 head0.485
GPT teacher head0.435
Teacher spread0.049 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations17
Published2023
Admission routes3
Has abstractyes

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