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Record W4411425395 · doi:10.1080/14767724.2025.2520949

Education toward cosmopolitanism as a pathway to reducing polarisation

2025· article· en· W4411425395 on OpenAlexaboutno aff
Eli Vinokur, Avinoam Yomtovian, Guy Itzchakov

Bibliographic record

VenueGlobalisation Societies and Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsnot available
Fundersnot available
KeywordsCosmopolitanismSociologyPolitical scienceSocial sciencePolitical economyPoliticsLaw

Abstract

fetched live from OpenAlex

Polarisation poses significant challenges to societal cohesion and democracy. This article explores how education, guided by cosmopolitan principles, can address these divides by fostering empathy, promoting intercultural dialogue, and challenging exclusionary narratives. This article advances the concept of rooted cosmopolitanism by demonstrating how local cultural attachments can complement global ethical principles by balancing particularistic and universalistic values. Through examples of case studies conducted in Canada, Israel, and India, this article highlights the adaptability of cosmopolitan education in diverse sociopolitical contexts and illustrates how education can bridge divides, promote mutual respect, and foster unity in diversity. The practical strategies include integrating global and local perspectives into curricula, promoting experiential learning to engage with diversity, and equipping educators with cultural competence and anti-bias tools. While resistance to change and resource constraints persist, the findings underscore education’s transformative potential to reduce polarisation and cultivate inclusive, equitable communities. This calls for sustained efforts to embed rooted cosmopolitan principles into education, by providing a framework for bridging divides and preparing students to navigate an interconnected world.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.020
GPT teacher head0.358
Teacher spread0.338 · 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 designNot applicable
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

Citations1
Published2025
Admission routes1
Has abstractyes

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