Memories of Mining: First Nation of Na-Cho Nyäk Dun Elders’ perspectives
Bibliographic record
Abstract
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”.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.014 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".