Green Energy—Green for Whom? A Case Study of the Kabinakagami River Waterpower Project in Northern Canada
Bibliographic record
Abstract
Green energy has become a term that heralds efforts of environmental conservation and protection worldwide; however, much of it is marred with questions of what it means to be green. More precisely, it has become a question of Green for whom? While many of the impacts of supposed green energy projects are local in their reach, some may be more regional in their scope, such as hydroelectric power. Hydroelectric power generation negatively impacts the environment and people who rely on the environment for sustenance, such as, Indigenous peoples of northern Canada. Taking into account their position with respect to the areas impacted by these green projects, many Indigenous peoples have voiced their concerns and doubts concerning green energy, which is purported to be a mode of energy production that champions the environment. The Kabinakagami River Waterpower Project serves as a case study for both the potential effects of the project and the different views associated with these endeavors. If nothing else, the accounts and testimonies found within shall stand as a testament to the hubris of calling an energy project green without properly assessing and considering the impacts. While these statements relate to the case presented, they also carry significance in the wider world due to the numerous Indigenous communities around the world that are having their spaces slowly being encroached upon in the name of sustainable growth, or green energy. This will especially be true in the post-COVID-19 period where green energy and a green economy are being touted as a way towards state and worldwide recovery.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.035 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".