Environmental conflict and the expansion of renewable energy in Central America: exploring Canadian participation
Bibliographic record
Abstract
As capacity to generate and distribute renewable energy expands across Central America, environmental conflict is on the rise, with communities confronting dispossession of water and territory. Renewable energy holds a central position within the extractivist economic model in the region, but little is known about the involvement of Canada in Central American renewable energy sectors. This paper addresses that gap by asking what role Canada plays within a scenario of expanding and conflictual renewable energy projects across Central America. We combine Canadian public records with data from projects, including GeoComunes and the Environmental Justice Atlas, to analyze 726 renewable energy and 162 traditional extractive projects in Central America, and to connect 156 of these projects to evidence of conflict with surrounding communities. While Canadian private companies own very few renewable energy projects in Central America, we argue that this does not imply an absence of Canadian involvement. Instead, the paper explores Canadian participation at multilateral venues, support for energy projects by the federal government, and the benefits that renewable energy expansion presents to the Canadian private sector. The invisibility of the Canadian presence in Central American renewable energy, we suggest, serves to obscure Canadian responsibility for a scenario defined by high levels of conflict.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".