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Record W4321376180 · doi:10.1177/11786302231157507

Honoring Indigenous Sacred Places and Spirit in Environmental Health

2023· article· en· W4321376180 on OpenAlexaff
Quanah Yellow Cloud, Nicole Redvers

Bibliographic record

VenueEnvironmental Health Insights · 2023
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsWestern University
Fundersnot available
KeywordsIndigenousEnvironmental ethicsMedicineBiologyEcologyPhilosophy

Abstract

fetched live from OpenAlex

Indigenous Peoples and their deep knowledges offer a fundamentally important way of seeing the world and the environment. Through relationships to distinct ancestral homelands, Indigenous Peoples have developed unique ways of surviving, adapting, connecting, and relating to their respective environments. Indigenous Sacred Places themselves are connections to ancestors, to all beings on the planet, and to different planes of existence. Sacred Places serve an important environmental role in many Indigenous Nations around the globe. Yet, Indigenous Sacred Places, and in particular understandings of spirit that connect Sacred Places, have been historically and contemporarily marginalized and excluded from environmental health academic discourse and spaces. This despite concrete calls for the amplification of Indigenous traditional knowledges-that of which does not separate spirit from knowledge, or spirit from action-they are intertwined. With this, we sought to amplify in this Perspective, understandings and connectivity between Sacred Places, spirit, and environmental health through the stories from Indigenous Elders, processes of ceremony, and personal synthesis.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0060.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.035
GPT teacher head0.340
Teacher spread0.305 · 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

Citations19
Published2023
Admission routes1
Has abstractyes

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