Nickel Rush: Indigenous Testimonies and Predictions about Mining from New Caledonia and Québec
Bibliographic record
Abstract
The rise of the electric vehicle industry will be accompanied with increased mining of two former French colonies, New Caledonia and Québec, both of which have historically been a great source of nickel despite Indigenous objection to nickel extraction. This thesis juxtaposes the novels of An Antane Kapesh, of Québec, and Claudine Jacques, of New Caledonia, to understand the Indigenous perspective of historical and future events of natural resource extraction and to see how these communities are impacted. Together they reveal that mining is a gendered act of violence that much like sexual assault, continues to have negative consequences long after the initial event. Not only do toxic chemicals from mining impact the vitality of Indigenous people, but additional substances are simultaneously introduced to further weaken them physically, emotionally, and spiritually. This ultimately leads to cultural genocide, which serves to sever the bond between Indigenous people and their ancestral land, hence allowing increased access to these spaces for colonial goals. This thesis demonstrates that the narratives of Jacques and Kapesh demand a revaluation of the intentions of self-proclaiming “green” and “Indigenous friendly” corporations, such as Tesla, as the great nickel rush of the twenty-first century commences. Advisor: Julia Frengs
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".