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Record W3056489702 · doi:10.1007/978-3-030-44617-8_11

Global Citizenship Education in Canada and the U.S.: From Nation-Centric Multiculturalism to Youth Engagement

2020· book-chapter· en· W3056489702 on OpenAlexaffabout
Sarah Ranco, Alexis Gilmer, Colleen Loomis

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsBalsillie School of International AffairsUniversity of WaterlooWilfrid Laurier University
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsGlobal citizenship educationOperationalizationCitizenshipMulticulturalismGlobal citizenshipCurriculumPolitical scienceContext (archaeology)PoliticsHegemonySociologyGender studiesCitizenship educationLawEpistemologyGeography

Abstract

fetched live from OpenAlex

Abstract This chapter examines the historical and current uses of global citizenship education (GCE) in Canada and the U.S. in public schools from primary through secondary levels, with attention to Canada as well as similarities and differences within and across the two countries. We assess how social and political contexts have influenced the definition and operationalization of multiculturalism, civic studies, and global studies in curricula, noting that the neo-liberal perspective has focused on making people an economic powerhouse rather than socially concerned global citizens. In our examination of educational approaches that relate to GCE, we present decolonizing pedagogies, the multiculturalism approach in Canada, as well as culturally responsive and anti-racist pedagogies. To illustrate these issues, we offer an example in the Canadian context and raise the need to prevent GCE from becoming yet another tool for hegemony by the Global North on the Global South, as dominant groups have long defined citizenship. We conclude by proposing that to realize GCE in these two countries, teacher/practitioner and local, national, and international actors must engage youth, and in doing so, power imbalances that prohibit becoming global citizens will be addressed.

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)0.000
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: Other · Consensus signal: none
Teacher disagreement score0.862
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.041
GPT teacher head0.287
Teacher spread0.246 · 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
GenreOther

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

Citations5
Published2020
Admission routes2
Has abstractyes

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