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Record W2508961906 · doi:10.18546/ijdegl.03.1.02

What Do We Ask of Global Citizenship Education? A Study of Global Citizenship Education in a Canadian University

2010· article· en· W2508961906 on OpenAlexaboutno aff
Lynette Shultz

Bibliographic record

VenueInternational Journal of Development Education and Global Learning · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsnot available
Fundersnot available
KeywordsCitizenshipSociologyGlobal citizenship educationGlobal citizenshipEquity (law)DemocracyUniversalismPoliticsPolitical scienceSocial scienceLaw

Abstract

fetched live from OpenAlex

This article presents findings from a study of a Canadian university that has named 'global citizenship' as a key educational goal. Drawing on theories of globalization, deliberative democracy, and deliberative processes including discursive closure, this study examines the multiple demands made of 'global citizenship' in higher education and the subsequent educational projects that are designed to meet this educational goal. The research questioned whether discursive closure was being engaged to limit 'global citizenship' to a modernity project where, as the literature suggested, (neo) liberalism and universalism ultimately served to make the world the un-gated playground of the elite where they might work, play, and consume without national or local political and cultural restrictions. In contrast, we wondered whether these policy openings might also be reflections of shifts in practices toward justice, equity, and inclusion with considerations of the historical and cultural histories and legacies of international relations of colonialism and imperialism. Using deliberative dialogue as a data collection method, the researchers were able to surface educators' multiple understandings of global citizenship as well as possible discursive closure and/or emerging social justice in the courses, projects, and policies of this institution.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.980

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 studies0.0000.000
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 head0.013
GPT teacher head0.333
Teacher spread0.319 · 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 designObservational
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

Citations18
Published2010
Admission routes1
Has abstractyes

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