Book Review: Teaching Global Citizenship: A Canadian Perspective
Bibliographic record
Abstract
Teaching Global Citizenship is a thoughtful and thought-provoking collection that provides sound philosophical and practical underpinnings for anyone looking to incorporate global citizenship education (GCE) into their classroom or to shift their practice towards one which is more place-responsive on a global scale.Written and edited by a mix of K-12 classroom teachers and post-secondary education faculty, the overarching themes of this book are evident even in the ways in which the editors opted not to homogenize the essays but, rather, to work with the assemblage of voices so that each chapter retains much of the narrative character and tone of the contributing authors.Through this editorial choice and the selected essay topics, readers gain a strong sense of how they too might connect themselves and their students to both the local (e.g., self) and the global in thoughtful and meaningful ways.This attention to whose voices, experiences, and lenses shapes the fields of CGE and education in-general is part of what makes this an exemplary resource for educators in all places and roles.GCE provides a coherent model for Social Studies education amidst the myriad competing aims of this interdisciplinary field; the CGE lens is one that features prominently in history and human geography curriculum in many Canadian provinces.Although presumably intended primarily for teachers of the humanities, given its dominant
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".