Global Citizenship in Higher Education: The Role of Academic Mobility
Bibliographic record
Abstract
The concept of Global Citizenship (GC) is frequently discussed in literature as a means of countering the effects of globalization. Higher education institutions are playing an increasingly prominent role in the field of Global Citizenship Education (GCE). It is essential that teachers possess the requisite skills and willingness to engage responsibly and effectively in a global environment, as they play a pivotal role in the dissemination of GCE. This contribution therefore quantitatively analyzes the extent of GC among teacher students and students of Social Sciences at German universities as a whole and as a function of experiences abroad during their studies (n=66). Using t-tests with independent samples and a one-factorial ANOVA, differences in the expression of GC are identified (1) based on whether an academic stay abroad was present and (2) based on the duration of the academic stay abroad. The data suggest that students who have had experience studying abroad tend to score higher along the three GC dimensions and in the total GC score compared to those who have not. It is noteworthy that students who spent the least amount of time abroad (2 to 8 weeks) scored the highest in GC, social responsibility, and global citizenship engagement. The results indicate that GC is a complex construct with several sub-dimensions, and it is not solely dependent on experience abroad.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".