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Record W7161964242 · doi:10.82308/44460

Federal loyalty in Canadian law: mitigating intergovernmental conflict during the Trans Mountain pipeline expansion project

2025· dissertation· en· W7161964242 on OpenAlexaboutno aff
Ashley Saad

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsOpposition (politics)FederalismLoyaltyNormativeExternalityConsolidation (business)

Abstract

fetched live from OpenAlex

Intergovernmental conflicts in Canada have intensified over the construction of new interprovincial pipelines designed to transport the country’s abundant oil and gas reserves to consumer markets. This thesis examines the intricate intergovernmental interactions at the policy, governance, constitutional, and regulatory levels in the pipeline context, using the Trans Mountain Expansion Project as a case study. It seeks to mitigate opposition between governments by proposing that courts be equipped with additional constitutional tools to promote cooperative behaviour. Specifically, this thesis advocates for incorporating a principle of federal loyalty into Canadian constitutional law. This principle could offer a normative foundation for the Supreme Court’s recent support of cooperative federalism by establishing rules to manage conflicts arising from intergovernmental collaboration. It would require federal partners to consult and cooperate with each other when exercising their otherwise legitimate powers to prevent harming or generating negative externalities for the other partners

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)0.013
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.163
Threshold uncertainty score0.971

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0540.022
Scholarly communication0.0150.004
Open science0.0030.006
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0040.000

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 head0.014
GPT teacher head0.302
Teacher spread0.289 · 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 designNot applicable
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
Published2025
Admission routes1
Has abstractyes

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