Global Citizenship Education from Across the Pacific: A Narrative Inquiry of Transcultural Teacher Education in Japan
Bibliographic record
Abstract
Teachers and teacher educators play increasingly important roles in creating successful futures for both individuals and society in light of globalization, increasing diversity, and growing interdependency. Within the dialectic of global and local, opportunities now for transformational learning, fostering social justice and global citizenship are unprecedented. However, global citizenship education (GCE) that explores different conceptual, theoretical, and methodological considerations of decolonizing citizenship education in practice remains a challenge for teachers. Thus, this paper describes pedagogies of GCE within pre-service teacher education classes and high schools in Japan. Furthermore, transcultural stories of several teachers are shared. The research reported here is based on over two decades of experience as a teacher educator in Canada and Japan. There is a paucity of long-term research on teachers’ reflections and experiences as they attempt to integrate GCE into their teaching, captured through personal narrative and story. Furthermore, due to a Western hegemony of knowledge, Eurocentric education, neo-colonialism, and neoliberal/conservative agendas in higher education, the voices of others outside North America are rarely heard. The research presented here attempts to fill this gap. This paper investigates these issues and teachers’ personal practical and professional knowledge through narratives of transcultural journeys.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.020 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".