Hydroelectricity, Environmental Governance and Anti-Reflexivity: Lessons from Muskrat Falls
Bibliographic record
Abstract
Hydroelectric projects are often pursued on the promise of economic development and environmental co-benefits as a source of low-carbon energy. We analyse the case of the Muskrat Falls hydropower mega-project (located in Labrador, Canada) to understand why this project failed to live up to its promised benefits, but instead delivered a double disaster of economic cost and environmental risk. The key concepts of anti-reflexivity and deep stories help us understand why the project assumed an aura of inevitability in political and public discourse until it was too late to change course. Drawing on publicly available data and secondary sources, we identify the constellation of social forces that maintained political anti-reflexivity about the economic and environmental risks of the project and led to a double economic and environmental disaster. Our analysis identifies vital lessons for countering anti-reflexivity and improving environmental governance related to energy mega-projects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".