Global Citizenship in Canadian Universities: A New Framework
Bibliographic record
Abstract
The value and importance of global learning is widely promoted and debated in the literature but, without a common language to frame this discussion, we cannot accurately assess its effectiveness or value.One term frequently used in these conversations, and extolled by universities, is the idea of global citizenship; however, there is no consistent definition of this concept.In this article, we describe the philosophical traditions surrounding the term global citizenship and explain the roots of the debate over its use.To further understand how this term is used among institutions of higher education, we investigated how select Canadian universities discuss global citizenship and identified some of the key terms used as proxies for it.By bringing together the existing academic literature, the available statistics, and a survey of mandates and practices across Canadian universities, we have developed a framework that defines a global citizen in a Canadian context.This shared framework, that universities can adapt and modify to meet their own institutional needs, is necessary to enhance their ability to develop the next generation of global citizens.A consistent language and vision will better shape the experiences students have, will ensure the evaluation of university programs is both possible and effective, and will create common goals that can be shared amongst industry, government, and universities.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.036 | 0.037 |
| Scholarly communication | 0.024 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".