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Record W2157076830 · doi:10.7202/1000622ar

Democratizing Common Law Constitutionalism

2011· article· en· W2157076830 on OpenAlexaffvenue
Evan Fox-Decent

Bibliographic record

VenueMcGill Law Journal · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsPrinciple of legalityConstitutionalismLawStatutory lawPolitical scienceJudicial reviewCommon lawPublic lawDemocracyAdministrative lawSources of lawLaw and economicsSociologyPolitics

Abstract

fetched live from OpenAlex

Common law constitutionalism is the theory that legal principles such as fairness and equality reside within the common law, are constitutive of legality, and inform (or should inform) statutory interpretation on judicial review. This article looks to Justice Rand’s judgment in Roncarelli v. Duplessis to develop a democratic and relational conception of common law constitutionalism. By “democratic” the author means a version of the theory that governs judicial review but which is available to frontline decision makers independently of the history and contemporary practice of review. By “relational” the author means a theory that presupposes a trust-like and legally significant relationship between public authorities and the persons subject to their power. Under the democratic and relational theory, the legality of administrative action is assessed in light of legal principles constitutive of the trust-like relationship and without reference to the separation of powers. These principles flow from the trust-like nature of the relationship and the implications of working out how public authorities can hold discretionary power over individuals without subjecting them to domination or instrumentalization.

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)0.024
metaresearch head score (Gemma)0.032
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 score0.024
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0050.033
Scholarly communication0.0090.010
Open science0.0020.009
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0040.001

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 head0.090
GPT teacher head0.321
Teacher spread0.231 · 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
Published2011
Admission routes2
Has abstractyes

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