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Record W2007449247 · doi:10.1515/1938-2545.1066

Rational Reasonableness: Toward a Positive Theory of Public Reason

2012· article· en· W2007449247 on OpenAlexaff
Gillian K. Hadfield, Stephen Macedo

Bibliographic record

VenueLaw & Ethics of Human Rights · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPublic reasonPoliticsLaw and economicsNormativeClassical liberalismEconomic JusticePolitical philosophyLiberalismPolitical scienceDemocracyIdeal theorySociologyLaw

Abstract

fetched live from OpenAlex

Abstract Why is it important for people to agree on and articulate shared reasons for just laws, rather than whatever reasons they personally find compelling? What, if any, practical role does public reason play in liberal democratic politics? We argue that the practical role of public reason can be better appreciated by examining the confluence of normative and positive political theory; the former represented here by liberal social contract theory of John Rawls and others, and the latter by rational choice or game theory. Citizens in a diverse society face a practical as well as a moral problem. How can they have confidence that others will reciprocate their commitment to supporting governing principles that depart from their own ideal conceptions of truth and value in order to be reasonable to all? Citizens face a practical problem of mutual assurance that public reason helps them solve, and solve as a matter of common knowledge. The solution, on both views, requires citizens’ reciprocal commitment to basing law on a system of shared reasons. Both views place public reason at the core of liberal democratic politics in conditions of diversity, and for quite similar reasons. Our argument illustrates the (often) complementary roles of positive and values-based analysis in constitutional design.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.494
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.088
GPT teacher head0.274
Teacher spread0.187 · 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.

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

Citations24
Published2012
Admission routes1
Has abstractyes

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