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Record W7054645708

Advocacy Coalitions and Canadian Energy Policy Decision: Navigating Four Pipeline Projects

2024· article· en· W7054645708 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2024
Typearticle
Languageen
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Pipeline (software)Process (computing)Energy policyIntersection (aeronautics)Action (physics)Energy (signal processing)
DOInot available

Abstract

fetched live from OpenAlex

The conflicting goals of sustaining the Canadian economy through energy and prioritizing climate action lead to diverse interest groups with varying views on policies advocating their positions to government. Despite energy's economic importance, climate change remains central for the Liberal government under Justin Trudeau. The management of pipeline proposals such as Transmountain, Northern Gateway, Energy East, Keystone Xl under this government showcased its balancing act between economic interests and environmental commitments. Applying the Advocacy Coalition Framework, this paper examines how governments and regulatory authorities modify the process for accepting or rejecting pipeline proposals, the evolution of interest groups in shaping their proposals, and the influence of this process on shaping their belief systems. The intersection involving the government, First Nations, environmentalists, provincial decisions, courts, media scrutiny, and the regulatory board's decision-making process explores the complex and contentious nature of approving major energy infrastructure projects, such as the Trans Mountain pipeline expansion.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.149
Threshold uncertainty score0.987

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0620.019
Scholarly communication0.0130.004
Open science0.0030.009
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0070.000

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 head0.018
GPT teacher head0.228
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2024
Admission routes1
Has abstractyes

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