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Record W4206595765 · doi:10.1139/facets-2021-0101

A century of knowledge: Kwakw<u>a</u>k<u>a</u>’wakw Elders and environmental change

2021· article· en· W4206595765 on OpenAlexvenueno aff
Tero Mustonen, Brie Van Dam, Hanna Eklund

Bibliographic record

VenueFACETS · 2021
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeIndigenousFlood mythPeriod (music)Traditional knowledgeGeographyPolitical scienceCorporate governanceColonial periodHistoryColonialismEthnologyEconomic historyArchaeologyEcology

Abstract

fetched live from OpenAlex

This paper reviews a century of Kwakwaka’wakw knowledge on ecological, climate, and social change. We trace the era of Indigenous governance (about the precolonial period), especially from about 1910 to the devastation of the flood in Dzawada’enuxw First Nation territory in Kingcome, British Columbia, in 2010. This time period has been chosen as the assessment period as this is the lifetime of the 10 Elders that we collaborated with to understand and position change during this tumultuous era. We call the results of this process “a century of knowledge”. Ecological, social, and climate change are positioned with scientific literature for potential divergence/convergence. Almost all aspects of the Kwakwaka’wakw home area have undergone large-scale changes including clear-cut forestry, salmon farms, climate change affecting species ranges, cultural impositions, and colonial processes working to destroy Indigenous governance. Despite these imposed changes, the communities emerge as survivors on their own terms, including using the traditional feast system known as the Potlatch to come to terms with the devastation of the 2010 flood and beyond.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.992
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.012
Scholarly communication0.0050.008
Open science0.0000.004
Research integrity0.0020.003
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 head0.051
GPT teacher head0.355
Teacher spread0.304 · 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.

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

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueFACETSSame topicIndigenous Studies and EcologyFrench-language works237,207