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Record W4412740643 · doi:10.22329/jtl.v19i5.9397

Cross-Cultural Competence in Pre-Service Teacher Education Towards Global Awareness: A Study in a Pakistani Context

2025· article· en· W4412740643 on OpenAlexvenueno aff
Bushra Jamil, Md Mirajur Rhaman Shaoan, Saida Irfan, Muhammad Ikbal Arif, Miracle Uzochukwu Okafor

Bibliographic record

VenueJournal of Teaching and Learning · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsnot available
Fundersnot available
KeywordsCultural competenceCompetence (human resources)Teacher educationPedagogyPsychologyContext (archaeology)Medical educationMedicineGeographySocial psychology

Abstract

fetched live from OpenAlex

Due to globalization, cultural awareness has been recognized as an essential factor in the context of teacher-education programs. The current research focuses on the role of cross-cultural training in increasing teachers' readiness for multicultural classrooms in Pakistan. This study employed a mixed-methods approach, collecting both quantitative and qualitative data through surveys and interviews with pre-service teachers. This investigation established a positive relationship between cross-cultural training, cultural diversity knowledge, and teacher self-efficacy in handling diversity. In addition to the above, regression analysis also confirmed that all these factors collectively explain a significant proportion of the variations in teacher preparedness. They integrated cross-cultural competencies in teacher-education curricula to prepare future instructors for diverse learners. The following recommendations are necessary to enhance the quality of teacher-preparation programs: the adoption of standardized assessments, the integration of fieldwork experiences, and regular professional development for teachers. This research contributes to the growing body of literature that advocates for a more cohesive approach to multicultural education, highlighting its significance in the educational process.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
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 integrity0.0000.001
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 head0.027
GPT teacher head0.436
Teacher spread0.408 · 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 designObservational
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

Citations2
Published2025
Admission routes1
Has abstractyes

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