Energy Justice and Canada’s National Energy Board: A Critical Analysis of the Line 9 Pipeline Decision
Bibliographic record
Abstract
This paper investigates the values and priorities reflected in a Canadian pipeline review: The National Energy Board (NEB) decision on Line 9. Theories of energy justice guided analysis of evidence presented at NEB hearings, the NEB’s explanation of its decision, and a Supreme Court challenge. We find that several aspects of energy justice were weak in the NEB process. First, a project-specific scope obstructed the pursuit of equity within and between generations: the pipeline’s contributions to climate change, impacts of the oil sands, and cumulative encroachment on Indigenous lands were excluded from review. Second, the NEB created a hierarchy of knowledge: it considered evidence of potential spill impacts as hypothetical while accepting as fact the proponent’s claim that it could prevent and manage spills. Third, recognition of diversity remained elusive: Indigenous nations’ dissatisfaction with the process challenged the NEB’s interpretation of meaningful consultation and procedural fairness. To address the challenges of climate change and reconciliation between Indigenous and settler nations, it is crucial to identify which kinds of evidence decision-makers recognize as valid and which they exclude. Ideas from energy justice can help support actions to improve the public acceptability of energy decisions, as well as to foster greater Indigenous autonomy.
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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 | 0.206 | 0.329 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.046 | 0.032 |
| Scholarly communication | 0.033 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.018 | 0.021 |
| Insufficient payload (model declined to judge) | 0.002 | 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".