Knowledge hierarchy and mechanisms of power in environmental impact assessment: Insights from the Muskrat Falls hydroelectric project
Bibliographic record
Abstract
Abstract Discussions surrounding the improvement of decision‐making processes like environmental impact assessment (EIA) often emphasize pluralism in the knowledge generation process, in an effort to increase effectiveness. However, empirical research indicates that such attempts to integrate multiple ways of knowing often fall short and significant knowledge conflicts remain. Scholars suggest this may be due to inadequate attention to power in the regulatory arena. The purpose of this study is to examine how power and knowledge influence the process and outcome of EIA in a participatory context. We develop a case study analysis of the Muskrat Falls hydroelectric project, located in Labrador, Canada. We use situational analysis to analyze documents produced over the course of the EIA and semi‐structured interviews. We find evidence of pluralism in knowledge production in the EIA process, but a distinct knowledge hierarchy in EIA outcome. We argue that this knowledge hierarchy is achieved through depoliticization of the EIA process. In particular, we identify the concept of “project inertia” as a distinct mechanism of depoliticization present in EIA and make policy prescriptions to improve the Canadian EIA process. This research contributes to broader theoretical discussions about pluralism in decision‐making processes.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".