MétaCan
Menu
Back to cohort
Record W3166461447

Biased Neutrality: The Supreme Court of Canada’s Unbalanced Attempt at Arbitration

2021· article· en· W3166461447 on OpenAlexaffabout
Dalraj Singh Gill

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSupreme courtLawPolitical scienceArbitrationGovernment (linguistics)FederalismLegislatureNeutralitySeparation of powersLaw and economicsSociologyPolitics
DOInot available

Abstract

fetched live from OpenAlex

The Supreme Court of Canada, with its judicial review powers, has been a central actor in interpreting and assigning certain powers to different levels of government and settling many constitutional debates. Although it is supposed to be a neutral and impartial party in the complex governance system, this article argues that the Supreme Court has constantly favoured the federal government’s interests and has not been a ‘balanced federal arbiter’. This has been exemplified by its tendencies to decide in the government’s favour, continued expansion of federal power, and instances of repressing minority and provincial interests. A statistical analysis of the government’s interventions in matters of constitutional significance and instances of overriding legislative boundaries to further federal interests demonstrates the unbalanced nature of the Court’s arbitration. These findings are concerning as they reveal an underlying preference for a Pan-Canadian vision of the Canadian federation rather than the multinational model sought by the Quebecois and Indigenous peoples.

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)0.027
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.101
Threshold uncertainty score0.736

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.054
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0240.019
Scholarly communication0.0160.003
Open science0.0020.004
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.278
Teacher spread0.249 · 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 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

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

Same topicPolitical Systems and GovernanceFrench-language works237,207