The Power of 'Net Zero': Seductive Dispossession on the Critical Minerals Frontier
Bibliographic record
Abstract
This article draws on insights gained from many years of community-engaged work alongside Neskantaga First Nation, a small remote Anishinaabe community in Treaty No.9, whose Indigenous homelands are being pressured by the global thirst for critical minerals. In line with recent writing on 'green extractivism', I detail how mining's new legitimacy in the boreal peatlands of the far north of Ontario, Canada, gained strength over the past decade from a pitch that associates it with battery metals for electric vehicles, and thus the transition to a 'net-zero' economy. The seduction obscures the social and ecological destruction that mining entails, and instead frames it as not only compatible with climate change, but crucial to our collective capacity to survive it. I argue that the power of net-zero is in the way it has provided a new, green economy rationale that shields old-economy extractivism from scrutiny to the detriment of the Indigenous stewards of lands and waters. The urgency of the climate crisis legitimizes the 'fast-tracking' of new critical minerals mining in a manner that overrides the inherent jurisdiction of Indigenous peoples, and their attempts to restore their territorial governing authority in line with their own laws. Major global geo-politics shifts are underway as this article goes to print, fueled by Trump 2.0's rejection of liberalized trade and the international climate order and his embrace of economic nationalism. As such, the seductive power of net-zero may already be diminishing, but over the past decade, it provided significant momentum to ongoing Indigenous dispossession in the boreal peatlands of Treaty No.9.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.066 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".