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Memories of Mining: First Nation of Na-Cho Nyäk Dun Elders’ perspectives

2020· article· en· W3085300315 on OpenAlexaboutno aff
Susanna Gartler, Gertrude Saxinger

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsViewpointsIndigenousNarrativePolitical scienceSociologyEcologyBiology

Abstract

fetched live from OpenAlex

This poster addresses the need to understand perspectives of change, both societal and environmental, from indigenous viewpoints in Canada. It is based on six years of collaborative, community-based research in Mayo, including semi-structured and narrative interviews with First Nation of Na-Cho Nyäk Dun Elders. Their accounts tell of over one century of interaction and involvement with the extractive industry. The poster addresses the way First Nation of Na-Cho Nyäk Dun Elders experienced and make sense of several major shifts, from settling at the onset of galena ore extraction, to life in and relocation from ‘Dän Ku’ (Our Home) to the townsite of Mayo, to life and work in Elsa and Keno – the mining hills nearby, which are home today to one of Canada’s largest gold mine. It discusses contemporary concerns with the industry, such as increased access to and thus pressure on wildlife due to mining roads, pollution, economic benefits and local employment. The poster further considers the methodological process which was centered on a community-based participatory approach. It is part of the outreach and science communication activities of the ReSDA (Ressources and Sustainable Development in the Arctic) funded project “LACE – Labour Mobiltiy and Community Participation in the Extractive Industry, Case Study in the Yukon”.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.280

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0360.014
Scholarly communication0.0070.003
Open science0.0020.008
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0060.000

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 head0.162
GPT teacher head0.390
Teacher spread0.229 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2020
Admission routes1
Has abstractyes

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