Mining impact and Indigenous protected and conserved areas
Bibliographic record
Abstract
Gold mining on pristine land that Indigenous people use for sustenance is a common practice in Canada, despite some of these lands being designated as Indigenous-protected areas. This study explores traditional land use protection versus natural resource extraction, looking at the Red Sucker Lake First Nation (RSLFN) region. I applied geographic information system mapping, analysis of transcribed audio interviews, and literature review methods in this study. Based on 21 map biographies of traditional land use of RSLFN interviewees’ transcripts focused on the preservation of traditional ecological knowledge (TEK), mining impacts, and traditional land use and occupancy (TLUO) of these 21 RSLFN people. Summary maps of the traditional land uses of 21 RSLFN people show sustenance and cultural activities on greenstone belts, designated by the province for mining. The interview analysis reveals exploration and mining activities impacting RSLFN’s traditional land and practices, causing spills and destroying personal property. The interviews also reveal community members’ desire to protect their land from mining activities for Indigenous knowledge preservation, ecosystem preservation, and traditional land use protection towards realizing Mino Bimaadiziwin (the good life). A change in governments’ policies on greenstone belts being restricted to mining development, which interferes with the traditional land use practices of affected Indigenous peoples, is needed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".