Book Review: <i>Power Struggles: Hydro Development and First Nationsin Manitoba and Quebec</i> Edited by ThibaultMartin and Steven M. Hoffman
Bibliographic record
Abstract
When First Nations try to protect their lands and waters it very often involves a struggle against some form of energy-related development. The greatest challenge facing those wishing to understand the long and complicated history between First Nations and hydro development in Canada is just that: it’s a very long and complex story. While this history begins over 50 years ago, the ensuing destruction of Indigenous lands and waters, cultures and ways of life, continues to this day. Many have believed the time of building new big dams was over, especially since the Report of the World Commission on Dams (WCD) in 2000 highlighted the often environmentally and socially devastating, and in many cases unnecessary, damages inflicted by large dams on local peoples. The WCD concluded that large dams should not be supported unless they result in a “significant advance of human development on a basis that is economically viable, socially equitable, and environmentally sustainable.” Nine years later, however, pressing calls for clean energy sources have combined with extensive “green-washing” of hydro development’s destructive effects to resurrect plans for hydro development (of all sizes) across Canada. The question remains to be answered, though, whether these new dams will result in the “ends” necessary for sustainable improvement of human welfare in Indigenous communities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.015 |
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".