Environmental Impact Assessment of Uranium Mining on Indigenous Land in Labrador (Canada): Biases and Manipulations
Bibliographic record
Abstract
Studies in Canada reveal the entrenched nature of the nation's mining paradigm that fundamentally undermines the interests of Indigenous peoples. However, very few research studies have explored the hidden biases and manipulations in the process of framing the Environmental Impact Assessment (EIA) of mines, particularly if developed on Indigenous land. The objectives of the study were to explore what biases and manipulations played roles in framing the EIA of uranium mining on Indigenous (Inuit) land in Labrador (Canada). The study analyzed all the archived documents (print and audio/video) related to the EIA process of the Kitts–Michelin project in Labrador (Canada). The EIA of the Kitts–Michelin project was poorly designed, with ill-planned public dissemination. The study demonstrates how hidden biases and manipulation in the entire process of EIA have served the purposes of certain interest groups and willfully neglected community concerns. The analysis of EIA reveals the institutionalization of biases and exclusionary processes and also exposes institutional racism that is running much deeper than merely prejudice. Although Inuit representatives attended the environmental review panel hearings, the decision makers were predominantly non-Indigenous (external consultants and members of the EIA review panel) and the final decision makers were always non-Inuit (and not local). The study shows that in-depth analysis of existing EIA along with the unpublished documents and audio and video records of panel hearings can provide a comprehensive understanding of racial, social, and environmental inequities associated with historical mining activities in Canada's Indigenous territories.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".