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Record W4316467068 · doi:10.1177/14687968231151455

Rethinking liberal multiculturalism: Foundations, practices and methodologies

2023· article· en· W4316467068 on OpenAlexaff
François Boucher, Sophie Guérard de Latour, Esma Baycan-Herzog

Bibliographic record

VenueEthnicities · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Philosophy and Ethics
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsMulticulturalismLegitimacySociologyNormativeLiberalismEpistemologyMinority rightsPolitical scienceEnvironmental ethicsSocial scienceLawPoliticsPhilosophy

Abstract

fetched live from OpenAlex

The article introduces a special issue on “Rethinking Liberal Multiculturalism: Foundations, Practices and Methodologies.” The contributions presented in this special issue were discussed during the conference « Multicultural Citizenship 25 Years Later », held in Paris in November 2021. Their aim is to take stock of the legacy of Kymlicka’s contribution and to highlight new developments in theories of liberal multiculturalism and minority rights. The contributions do not purport to challenge the legitimacy of theories of multiculturalism and minority rights, they rather aim at deepening our understanding of the foundations of liberal multiculturalism and of its practical implementation, sensitive to social scientific dynamics of diverse societies. Without abandoning the general idea that cultural minorities should be granted special minority rights, the essays presented raise new questions about three dimensions central to liberal multiculturalism: its normative foundations, its practical categories of minorities or groups, and its fact-sensitive methodology. Taken together they shed light on the renewed variety of theories of liberal multiculturalism highlighting their complexity and internal disagreements. To introduce these articles, the article first draws a brief historical overview of the debates on multiculturalism since the 1990s (section 1). It then highlights the distinctive aspects of Kymlicka’s contribution (section 2) and identifies recent research trends (section 3). Doing so, it explains how the articles gathered here both expand on those distinctive aspects and explore those new research avenues. The section 4 summarizes the contributions.

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)0.002
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.449
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.471
GPT teacher head0.498
Teacher spread0.027 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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