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Record W2913223613 · doi:10.3390/su11030783

Energy Justice and Canada’s National Energy Board: A Critical Analysis of the Line 9 Pipeline Decision

2019· article· en· W2913223613 on OpenAlexafffundabout
Carol Hunsberger, Sākihitowin Awâsis

Bibliographic record

VenueSustainability · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsWestern University
FundersWestern UniversitySuncor Energy Incorporated
KeywordsIndigenousEquity (law)Economic JusticeAutonomyPolitical scienceEnvironmental justiceSupreme courtEnvironmental resource managementSociologyEnvironmental ethicsLaw and economicsLawEconomicsEcology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.516
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.301
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations24
Published2019
Admission routes3
Has abstractyes

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