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Record W1508160710 · doi:10.63997/jct.v25i2.84

Globalization in the Classroom: Students, Citizenship and Consumerism

2025· article· en· W1508160710 on OpenAlexaboutno aff
Robert E. White

Bibliographic record

VenueJournal of Curriculum Theorizing · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsnot available
Fundersnot available
KeywordsConsumerismCitizenshipGlobalizationPolitical scienceSociologyGlobal citizenshipPedagogyLawPolitics

Abstract

fetched live from OpenAlex

This paper discusses implications of globalization as it impacts educational practices, procedures, and policies. Because economic competition on a global scale encourages corporations to influence educators to replicate dominant culture values within classrooms, these patterns serve to create and valorise power differentials that prevent society members, including students, from participating fully in all aspects of a democratic society. A critical pedagogy beginning with Habermas' Ideal Speech Situation may serve to question practices, procedures, and policies that preserve power differentials that prevent students from developing their capacities for becoming agents of positive social change. About the Author Robert White has taught extensively in public school systems across Canada and is currently an Associate Professor at St. Francis Xavier University in Nova Scotia. Research interests include critical literacy, learning and leadership, globalization, and corporate involvement in educational settings. His most recent books include Burning Issues: Foundations of Education (2004), The Practical Critical Educator: Critical Inquiry and Educational Practice (2005), and Critical Literacies in Action: Social Perspectives and Teaching Practices (2008).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.018
Scholarly communication0.0100.006
Open science0.0000.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.016
GPT teacher head0.362
Teacher spread0.347 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2025
Admission routes1
Has abstractyes

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