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Record W4280601503 · doi:10.1111/anti.12848

Pipelines in the “Public Interest”? The Jurisdictional Work of a Concept in Canadian Pipeline Assessment

2022· article· en· W4280601503 on OpenAlexafffundabout
Liam Fox

Bibliographic record

VenueAntipode · 2022
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsJurisdictionPublic interestPoliticsMandateIndigenousPublic administrationState (computer science)Work (physics)Law and economicsPolitical scienceNational interestPipeline (software)LawBusinessEngineeringEconomicsComputer science

Abstract

fetched live from OpenAlex

Abstract This paper contributes to research on the means by which the Canadian state authorises, enables, and secures new major petroleum pipelines. While disruptions to pipeline construction are now increasingly common, state and industry alike have continued to finance and approve new projects, even despite serious and ongoing concerns about impacts on ecologies and Indigenous jurisdiction. The paper focuses on one under‐researched mechanism of state authorisation: federal impact assessments for new oil and gas pipeline projects, undertaken by the National Energy Board (NEB), Canada’s now former energy regulator, which operated between 1959 and 2019. The NEB’s mandate was to deem, via impact assessment, whether a new project would be in the “public interest”. I argue public interest is an effective legal‐political mechanism for securing and obscuring the state’s claim to jurisdiction, the traction of which lies partly in an underlying colonial scalar logic and imaginary.

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)0.021
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.982
Threshold uncertainty score0.615

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0180.077
Scholarly communication0.0200.012
Open science0.0030.007
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0080.000

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 head0.021
GPT teacher head0.242
Teacher spread0.221 · 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.

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

Citations1
Published2022
Admission routes3
Has abstractyes

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