Producing consent: How environmental assessment enabled oil and gas extraction in the Qikiqtani region of Nunavut
Bibliographic record
Abstract
There is now an extensive body of academic literature examining how the environmental movement contributed to the colonization of Indigenous peoples and development of capitalism in northern Canada. This paper contributes to these discussions by considering how environmental assessment (EA) helped enable hydrocarbon extraction in the Qikiqtani (Baffin Island) region of Nunavut in the 1970s and 1980s. When exploration activities began to threaten the Inuit harvesting economy, communities protested with letters and petitions. The federal government responded to Inuit resistance by referring proposed exploratory drilling and extraction to its new EA process. While Inuit won significant victories during some assessments of proposed exploratory drilling and extraction, federal EA ultimately helped create the conditions for Inuit to consent to oil extraction. EA helped impose material compromises between Inuit and hydrocarbon industries, including preferential hiring of Inuit, a reduction in the scope of proposed extraction, and the rejection of especially controversial proposals for offshore drilling. These concessions, combined with a collapse in the market for sealskins due to international boycotts, persuaded several Qikiqtani communities to support oil extraction in the 1980s. The ensuing extraction and export of oil from the High Arctic accelerated processes of colonial dispossession and reinforced colonial political dynamics.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".