What Do We Ask of Global Citizenship Education? A Study of Global Citizenship Education in a Canadian University
Bibliographic record
Abstract
This article presents findings from a study of a Canadian university that has named 'global citizenship' as a key educational goal. Drawing on theories of globalization, deliberative democracy, and deliberative processes including discursive closure, this study examines the multiple demands made of 'global citizenship' in higher education and the subsequent educational projects that are designed to meet this educational goal. The research questioned whether discursive closure was being engaged to limit 'global citizenship' to a modernity project where, as the literature suggested, (neo) liberalism and universalism ultimately served to make the world the un-gated playground of the elite where they might work, play, and consume without national or local political and cultural restrictions. In contrast, we wondered whether these policy openings might also be reflections of shifts in practices toward justice, equity, and inclusion with considerations of the historical and cultural histories and legacies of international relations of colonialism and imperialism. Using deliberative dialogue as a data collection method, the researchers were able to surface educators' multiple understandings of global citizenship as well as possible discursive closure and/or emerging social justice in the courses, projects, and policies of this institution.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.067 | 0.035 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".