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Record W4404708555 · doi:10.14321/aehm.027.02.64

Indigenous ways of being and the Ecosystem Approach

2024· article· en· W4404708555 on OpenAlexaff
Shayenna Nolan, Alexander T. Duncan, Candy Donaldson, Clint Jacobs, Anthony “Miptoon” Chegahno, Karen Cedar, Bkejwanong Eco-Keepers

Bibliographic record

VenueAquatic Ecosystem Health & Management · 2024
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsWindsor Utilities Commission (Canada)University of British ColumbiaUniversity of Windsor
Fundersnot available
KeywordsIndigenousEcosystemGeographyEcologyEnvironmental resource managementAgroforestryEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Abstract Indigenous knowledge systems, ways of knowing and being have long been ignored or erased in the science, policy, and management of ecosystems. Through the 2022 Ecosystem Approach Conference and Synthesis Workshop focusing on Indigenous relationships, we facilitated discussions which raised key concerns from Indigenous led environmental teams: funding, collaborations, work/life balance, Indigeneity/Western/colonial balance, and racism. We discuss how conceptions of the Ecosystem Approach are synonymous with Indigenous management by definition and practice. Drawing on specific examples raised in workshop discussions and from the literature, we highlight how holistic approaches to the caring for and science of ecosystems have long been the way of Indigenous communities locally, globally, and across generations. To elevate these approaches and support this holistic vision of having relationship with ecosystems, we collectively call for better avenues of funding and responsive structures to support Indigenous-led initiatives. This requires that first we recognize Indigenous sovereignty and as settler and non-Indigenous scientists we invest in and maintain real relationships by listening to and standing with Indigenous Peoples in an effort to better support care for their Lands, Waters, and Kiin.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.482
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.043
GPT teacher head0.331
Teacher spread0.287 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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