Bibliographic record
Abstract
Abstract This chapter reports how educating for global citizenship is being practiced by teachers in the K–12 education landscape in three metropolitan regions of Canada. First, we briefly review the history of global citizenship education in Canada to contextualize the foundation upon which educating for global citizenship is being understood and practiced by teachers. Next, we introduce a series of conceptual frameworks that synthesize common priorities linked to educating for global citizenship in educational policy, practice, and research communities. Lastly, we report findings from a qualitative research study that we conducted between 2008 and 2011 on educating for global citizenship from the perspectives of Canadian public school teachers from three metropolitan regions. This study investigated teachers’ learning goals, instructional practices, and orientations when educating for global citizenship and how each contributes to the other. In this chapter we focus on teachers’ instructional practices though we consider these alongside their stated learning goals and orientations in order to broaden our analysis to not only consider how participating teachers’ educate for global citizenship, but also why. We report that across regions and methods of participation, although to varying degrees, teachers involved in this study reported using instructional practices oriented to teaching for worldmindedness, civic action, and to a slightly lesser extent, critical literacy. We conclude the chapter with a discussion of the findings and their implications for educational policy, practice, and research communities.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.040 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".