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Record W4388888613 · doi:10.59015/wilj.sidc2237

Counter Currents to the Globalization of Proportionality

2023· article· en· W4388888613 on OpenAlexaboutno aff
Shiling Xiao

Bibliographic record

VenueWisconsin International Law Journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Labor and Employment Law
Canadian institutionsnot available
Fundersnot available
KeywordsProportionality (law)GlobalizationPolitical scienceLaw

Abstract

fetched live from OpenAlex

There has been extensive literature on proportionality that praises its global spread and delves into its embedment in domestic contexts. Through a series of case studies from around the world, however, this Article shows judicial practices countering, explicitly or implicitly, the popular notion of globalization of proportionality. In Canada and the United Kingdom, the Supreme Courts have directed the courts to depart from proportionality and rely on unreasonableness as a default standard of judicial review of administrative decisions. Courts in Hong Kong and Taiwan have developed various levels of intensity of review associated with proportionality; the lowest of which transforms proportionality, a rights protective doctrine, into a form of weak reasonableness review or deferential rational-basis review. The Chinese courts have fashioned proportionality into a tool to justify illegal government actions and advance authoritarian projects. This Article describes these judicial practices as counter currents of proportionality and argues that they are not merely variations of proportionality used in local contexts; instead, they have either deserted the doctrine entirely or contradicted its core features: (1) structured analytical procedure and (2) high-level human rights protection. In response to these counter currents, this Article proposes that once a court adopts proportionality in its adjudication, it must adhere to the structured analytical framework and insist on a minimum rigor of this doctrine.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.937
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.376
Teacher spread0.337 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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