Review of First Nations Issues and Mining in Canada
Bibliographic record
Abstract
Decisions on a considerable number of mining projects in Canada are being affected by issues related to First Nations communities. This paper will review First Nations issues with respect to resource development. The historical nature of these issues is requiring mining companies to rethink how they approach mine projects especially with respect to community engagement and value-sharing. Examples of successful engagement and unsuccessful efforts will be presented. How long have I known you, Oh Canada? A hundred years?...And today, when you celebrate your hundred years, Oh Canada, I am sad for all the Indian people...For I have known you when your forests were mine; when they gave me my meat and my clothing. I have known you in your streams and rivers where your fish flashed and danced...where the waters said '...come and eat of my abundance.' I have known you in the freedom of the winds. And my spirit, like the winds, once roamed your good lands...in the long hundred years since the white man came, I have seen my freedom disappear like the salmon going mysteriously out to sea. The white man’s strange customs...pressed down upon me until I could no longer breathe. When I fought to protect my land..., I was called a savage. When I neither understood nor welcomed his way of life, I was called lazy. When I tried to rule my people, I was stripped of my authority. My nation was ignored in your history textbooks – they were ...(as)...important ... (as)...the buffalo that ranged the plains. I was ridiculed in your plays and motion pictures, and when I drank your fire-water, I got drunk... And I forgot. from Lament for Confederation, Chief Dan George, July 1, 1967.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.041 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".