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Record W4381431771 · doi:10.1093/icon/moad025

Theorizing about the executive in the modern state

2023· article· en· W4381431771 on OpenAlexaff
Vanessa MacDonnell

Bibliographic record

VenueInternational Journal of Constitutional Law · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicJudicial and Constitutional Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPrinciple of legalityPluralLawState (computer science)Separation of powersCorporate governancePower (physics)Judicial reviewLaw and economicsDemocracyExecutive branchPolitical scienceSociologyEpistemologyPoliticsComputer scienceEconomicsPhilosophyManagement

Abstract

fetched live from OpenAlex

Abstract Margit Cohn has written a terrific new book about the executive branch of state. Writing against the backdrop of a wave of executive aggrandizement in constitutional democracies around the world, Cohn sets herself the ambitious task of constructing a theoretical account of executive power. What emerges is a theory built around the twin themes of tension and legality. In most constitutional systems, she argues, the law provides broad general authorization for executive action, leaving the state with a wide margin to maneuver while still being able to brandish the mantle of legality. For Cohn, this raises rule of law and democracy concerns. In this review essay, I suggest that we should follow Cohn’s twin themes through to their logical conclusion. Perhaps controversially, I suggest that where this takes us is not exactly where Cohn ends up. Rather than countering strong executives with strong courts, we should adopt the plural approach Cohn mentions in passing at the outset of the book. This means exploiting institutions’ strengths and attempting to respond meaningfully to their weaknesses. In doing so, we must be careful not to adopt responses to executive power that erect unnecessary obstacles to governance in the public interest.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.942
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.342
Teacher spread0.304 · 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 teacher head, not a consensus.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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