Bibliographic record
Abstract
Using data drawn from their websites this chapter displays just how pervasive the use of the concept of global citizenship is across Canadian universities’ administrations, faculties, departments, programmes and courses. For ease of presentation the uses are grouped into the following categories: definitions of global citizen, vision/mission/strategy, programmes/courses/clubs/, events, international student opportunities, certifications/awards/donations, global citizenship education and research, student testimonials, and study abroad/international service learning (ISL). Particular attention is given to international service learning. Following Chapman (2016) the data are then analytically considered from a political-economic perspective to elucidate what these uses amount to, what they signify. Such consideration demonstrates the vacuousness of the concept, the privilege it carries and the ideological service it performs for neoliberal capitalism. The average Southern student will never be considered a global citizen because they will not have the resources to ‘save the world’, ‘travel the world’ or simply to participate and commit to improving the world beyond their borders. In fact, the Northern ‘Global Citizen’ can be said to embrace the concept at the expense of the communities in the global South that come to be products or commodities benefiting the North. Some contrary views are taken up and discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.016 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".