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Record W2966738175

Spatial Statism (Foreword)

2019· article· en· W2966738175 on OpenAlexaff
Ran Hirschl, Ayelet Shachar

Bibliographic record

VenueSSRN Electronic Journal · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Law and Human Rights
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSovereigntyDemisePolitical scienceState (computer science)International lawLawPublic spaceStatismLaw and economicsPublic lawPolitical economySociologyPolitics
DOInot available

Abstract

fetched live from OpenAlex

In this Foreword article, we wish to insert a degree of innovation into debates about global law and the supposed demise of state-based sovereignty and public law. We do so by asking how considerations of space, place and density impact the conceptualization and utilization of state power in a world of growing complexity and interdependence. In an array of key policy areas (immigration regulation and border control; the constitutional status of cities; natural resources; the place of religious symbols in the public sphere; and “us vs. them” constructions of national identity), we examine how state-centered public law defines, and where required redefines, space and territory in order to tame potential threats—local or global—to the state’s territorial sovereignty. Our exploration highlights the tremendous versatility and creativity of states in deploying and stretching, through the classic tools of public law, their spatial and juridical tentacles in a new and complex global environment. Taken in conjunction, these illustrations suggest that the disregard for and dismissal of the state as a potent actor in the public law arena is premature. State sovereignty may be metamorphosing, but it is evidently not vanishing.

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)0.001
metaresearch head score (Gemma)0.003
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.207
Threshold uncertainty score0.692

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.2070.088

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 head0.006
GPT teacher head0.265
Teacher spread0.259 · 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
GenreCommentary

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
Published2019
Admission routes1
Has abstractyes

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