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Record W4408694008 · doi:10.5206/uwojls.v16i1.22166

Taking Proportionality Seriously: A Countercultural Approach

2025· article· en· W4408694008 on OpenAlexvenueaboutno aff
Suzanne Chiodo

Bibliographic record

VenueWestern Journal of Legal Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Peace and Security Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsProportionality (law)PsychologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Proportionality was introduced into Ontario’s Rules of Civil Procedure in 2008. A plain reading of Rules 1.04(1) and (1.1) is the ‘traditional’ interpretation of proportionality: that proportionality is merely the means of furthering efficiency and economy in individual cases, with the aim of better securing substantive justice on the merits in those cases. The relative measure is what is at stake in the individual case. This article suggests a counter-cultural interpretation: that proportionality is equal in importance to substantive justice and that, in certain circumstances, substantive justice in individual cases must give way to proportionality, in order to secure the just distribution of resources for all cases. The relative measure is what is at stake for the civil justice system as a whole, with justice defined more collectively. This article posits that proportionality can effect a change in our legal culture, but only if we accept a countercultural definition of that word.

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)0.017
metaresearch head score (Gemma)0.028
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 score0.025
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0140.073
Scholarly communication0.0140.014
Open science0.0020.012
Research integrity0.0050.012
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 head0.048
GPT teacher head0.401
Teacher spread0.353 · 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
Published2025
Admission routes2
Has abstractyes

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