Contested notions of energy justice and energy futures in struggles over tar sands development in British Columbia, Canada
Bibliographic record
Abstract
This paper explores the articulation and mobilization of competing notions of energy (in) justice and energy future visions in the struggle over the Trans Mountain pipeline expansion project (TMX) in British Columbia, Canada. The TMX is a controversial fossil fuel project because it leads to the appropriation of First Nations lands, gender violence, and the unequal distribution of the socio-environmental costs of tar sands operations. Despite these impacts, the Canadian state argues that the TMX contributes to economic growth, job creation, and increased tax revenues, thereby legitimizing tar sands expansion on Indigenous lands. Drawing upon literature on Indigenous climate/energy justice, we problematize conventional understandings of energy justice and energy futures by examining multiple, interconnected, and often neglected dimensions of justice in the TMX conflict. Through critical discourse analysis, this paper explores how First Nations opposing this pipeline mobilize different notions of justice to envision alternative energy futures. Our study shows how they challenge Canada’s fossil fuel future vision by asserting jurisdiction over the lands crossed by the TMX and demanding the cancellation of this pipeline. Centering counter-hegemonic perspectives in discussions about tar sands development provides a starting point for imagining and building more just energy futures.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.057 | 0.034 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".