MétaCan
Menu
Back to cohort
Record W4400828808 · doi:10.1002/pan3.10688

Gugwilx'ya'ansk and goats: Indigenous perspectives on governance, stewardship and relationality in mountain goat (mati) hunting in Gitga'at territory

2024· article· en· W4400828808 on OpenAlexafffund
Spencer Greening

Bibliographic record

VenuePeople and Nature · 2024
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsAssembly of First NationsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of CanadaVancouver FoundationSimon Fraser UniversityPierre Elliott Trudeau Foundation
KeywordsIndigenousStewardship (theology)Corporate governanceEnvironmental ethicsIndigenous cultureGeographyPolitical scienceEthnologySociologyManagementBiologyEcologyLawPhilosophy

Abstract

fetched live from OpenAlex

Abstract Indigenous peoples' deep time relationships with ecosystems hold valuable lessons on how humans can relate to, and be stewards in, the natural world. At the crux of these lessons is the multifaceted way Indigenous peoples participate within ecosystems. This paper describes this multifaceted connection between people and place by analysing a legal and pedagogical philosophy called gugwilx'ya'ansk amongst the Ts'msyen (Tsimshian) people of the northwest coast of North America. The author, an Indigenous anthropologist from the Gitk’a’ata (Gitga'at) tribe of the Tsimshian, narrates how gugwilx’ya’ansk weaves education, governance, identity, spirituality, and ritual into land‐based practices for the purpose of deep‐time stewardship. Through autoethnographic narrative and storytelling, he focuses on his own journey of being groomed into becoming a mountain goat hunter within the hereditary governance system of his community, and how this process revealed a methodology to achieve relationality and reciprocity on the landscape while harvesting. This paper concludes by reflecting on why this Indigenous methodology has been successful for the author, and what lessons it has to offer greater society. Read the free Plain Language Summary for this article on the Journal blog.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0010.001
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 head0.011
GPT teacher head0.328
Teacher spread0.318 · 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 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

Citations7
Published2024
Admission routes2
Has abstractyes

Explore more

Same venuePeople and NatureSame topicIndigenous Studies and EcologyFrench-language works237,207