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Record W2784224804 · doi:10.1057/978-1-137-59733-5_4

Global Citizenship Education in North America

2018· book-chapter· en· W2784224804 on OpenAlexaff
Carla L. Peck, Karen Pashby

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2018
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsAlberta Advanced EducationUniversity of Alberta
Fundersnot available
KeywordsCitizenshipGlobal citizenship educationCurriculumContext (archaeology)MulticulturalismArgument (complex analysis)Political scienceCitizenship educationGlobal citizenshipMulticultural educationPopulationSociologyPedagogyGender studiesGeographyLawDemography

Abstract

fetched live from OpenAlex

To begin to considerPeck, C. L. the context of globalPashby, K. citizenshipCitizenship education in North AmericaNorth America , it is important to look at some key characteristics of the continent. In this chapter, we will emphasize the relationship between North America’s multicultural population and multicultural policies and the content and pedagogyPedagogy connected to global education. We will start with some key characteristics of the North American context and will link the historyHistory of multiculturalismMulticulturalism and global educationEducation . Then we will look specifically at global citizenship education (GCE) trends within North AmericaNorth America . Our focus in this chapter is on the theoretical and empirical literature on global citizenship education in elementaryElementary and high schools (and not including higher educationHigher education ) in CanadaCanada and the USA as well as some reflectionsReflection on some of the issues arising principally from English-languageLanguage literature from MexicoMexico . We conclude with an argument for the importance of mobilizing around a critical approach which is already occurring but that requires more work in curriculum, pedagogyPedagogy , and research.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.025
GPT teacher head0.307
Teacher spread0.282 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations6
Published2018
Admission routes1
Has abstractyes

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