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Record W2810972976 · doi:10.5430/wje.v8n3p139

Transformative Citizenship Education and Intercultural Sensitivity in Early Adolescence

2018· article· en· W2810972976 on OpenAlexvenueno aff
Jennifer M. Mellizo

Bibliographic record

VenueWorld Journal of Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsnot available
Fundersnot available
KeywordsTransformative learningCurriculumPedagogyPsychologyCitizenshipCitizenship educationPsychological interventionMathematics educationPolitical science

Abstract

fetched live from OpenAlex

As our world continues to evolve into an increasingly diverse, interconnected, and interdependent global society, it isbecoming more important for tomorrow’s citizens (today’s early adolescent students) to develop the knowledge,skills, and dispositions they will need to understand and communicate with individuals who come from many diversecultural backgrounds. Yet, relatively few researchers have examined the effects of specific curriculum interventions,strategies, and/or educational approaches designed to improve intercultural knowledge, skills, and attitudes duringearly adolescence. In this study, a causal-comparative quantitative research design was used to explore differences inintercultural sensitivity between a group of 4th–6th grade students at a school that embraces a transformativeapproach to citizenship education (School 1), and a group of students at a comparison school (School 2). Anindependent t–test revealed students at School 1 scored significantly higher than students at School 2 on aquantitative measure of intercultural sensitivity (AISI). These results suggest a transformative approach to citizenshipeducation can promote the development of intercultural sensitivity during early adolescence. In light of these results,several key aspects of this particular school’s transformative citizenship curriculum are discussed in detail.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.751
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.021
GPT teacher head0.344
Teacher spread0.322 · 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 designQualitative
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

Citations5
Published2018
Admission routes1
Has abstractyes

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