Bibliographic record
Abstract
To begin to considerPeck, C. L. the context of globalPashby, K. citizenshipCitizenship education in North AmericaNorth America , it is important to look at some key characteristics of the continent. In this chapter, we will emphasize the relationship between North America’s multicultural population and multicultural policies and the content and pedagogyPedagogy connected to global education. We will start with some key characteristics of the North American context and will link the historyHistory of multiculturalismMulticulturalism and global educationEducation . Then we will look specifically at global citizenship education (GCE) trends within North AmericaNorth America . Our focus in this chapter is on the theoretical and empirical literature on global citizenship education in elementaryElementary and high schools (and not including higher educationHigher education ) in CanadaCanada and the USA as well as some reflectionsReflection on some of the issues arising principally from English-languageLanguage literature from MexicoMexico . We conclude with an argument for the importance of mobilizing around a critical approach which is already occurring but that requires more work in curriculum, pedagogyPedagogy , and research.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".