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Record W4393391969 · doi:10.5206/cie-eci.v53i1.16592

Reconceptualizing Global Citizenship in Turkish Social Studies Textbooks With a Focus on Social Justice

2024· article· en· W4393391969 on OpenAlexaffvenue
Emin Kılınç, Ardavan Eizadirad, Jennifer M. Straub

Bibliographic record

VenueComparative and International Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsTurkishCitizenshipFocus (optics)Social justiceSociologyPolitical scienceSocial scienceLawLinguisticsPhilosophyPolitics

Abstract

fetched live from OpenAlex

This study examines the integration of global perspectives in the social studies textbooks used in Grades 4 to 7 in Turkey using content analysis as a methodology. The analysis focuses on seven mandatory textbooks distributed across the country and the big ideas and themes reinforced as official knowledge promoted by the state. The findings reveal both strengths and weaknesses in the textbooks’ content and approach to teaching about global connections. While the textbooks aim to promote global citizenship, cultural understanding, and economic relationships, they also perpetuate nationalistic perspectives, stereotypes, and biased coverage of various forms of inequality and social injustice. Failure to critically analyze diverse cultures, perpetuation of biased views, negative portrayal of Western culture, and a lack of comprehensive coverage of inequality are some of the arising issues and emerging themes identified. Additionally, the textbooks neglect explicit discussions about equity and social justice and connections to the civic engagement of citizens. These findings promote passive citizenship and underscore the need for greater attention to inclusivity, cultural understanding, and comprehensive coverage of global issues and social justice in social studies education in Turkey and beyond as it relates to international relations.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.344

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 studies0.0000.000
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 head0.454
GPT teacher head0.537
Teacher spread0.083 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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