Questioning Global Citizenship Education in the Internationalization Agenda
Bibliographic record
Abstract
The present paper provides a reflection on global citizenship education (GCE) in the internationalization agenda. With that aim, the internationalization of higher education (IHE) is discussed from a critical perspective, mainly informed by postcolonial and decolonial studies. More specifically, the paper addresses GCE issues related to criticisms that have been raised against it in terms of (1) its different educational approaches, (2) its cosmopolitan bias with its (3) ideological frame of the so-called “global citizen”. Some alternatives to mainstream approaches to GCE and IHE are offered in the conclusion, based on the contributions of Stein (2017), Andreotti (2015) and Fiedler (2007), who advocate for the otherwise approach and/or postcolonial learning spaces.
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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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| 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".