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Record W1976663592 · doi:10.1093/icon/mou060

An egalitarian defense of proportionality-based balancing

2014· article· en· W1976663592 on OpenAlexaff
L. B. Tremblay

Bibliographic record

VenueInternational Journal of Constitutional Law · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Philosophy and Ethics
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsProportionality (law)NormativePluralism (philosophy)Argument (complex analysis)VirtueEpistemologyLaw and economicsSociologyLawPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

In this article, I argue that there are two distinct conceptions of the principle of proportionality within constitutional law and theory. One comes within what I call “the priority of rights model” and the other within what I call “the model of optimization of values in conflict.” The main purpose of the text is to propound an egalitarian defense of proportionality-based balancing, such as it is understood within the model of optimization. After having described the “idealtype” of each model (Section 2), I argue that the normative justification of the model of optimization lies in the fact that it honors the moral equality of persons in context of pluralism and cultural diversity (Section 3). In particular, I argue that, from this normative point of view, the priority of rights model has become difficult to sustain. The argument is a form of “ethical proceduralism”: it refers to a procedure by virtue of which the normative validity of the competing models is tested.

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)0.019
metaresearch head score (Gemma)0.024
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 score0.019
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0040.036
Scholarly communication0.0080.014
Open science0.0040.008
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0070.002

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 head0.035
GPT teacher head0.363
Teacher spread0.328 · 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

Citations21
Published2014
Admission routes1
Has abstractyes

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