Internationalisation of Higher Education and Its Political Dynamics
Bibliographic record
Abstract
To thrive in multicultural workplace settings, it is essential to understand how individuals from different cultural backgrounds communicate and share meaning. Higher education institutions (HEIs), through international academic collaborations, are uniquely positioned to cultivate a skilled workforce capable of addressing contemporary global challenges. Beyond fostering cultural awareness, a critical and analytical approach has become integral to modern higher education, ensuring a rigorous and versatile learning environment that promotes respect for diverse needs and rights. This chapter provides a critical analysis of the factors shaping the internationalisation of higher education within the Global North and South. It examines current trends in curriculum internationalisation and suggests strategies to enhance global competence among graduates. In an era defined by open science and advancements in artificial intelligence (AI), issues of equality, diversity, and inclusion (EDI) have taken centre stage in education. This chapter proposes a Universal Design for Learning (UDL) framework to address these challenges and demonstrate how socio-cultural and geopolitical perspectives can be harnessed to promote internationalisation within higher education curricula. The chapter concludes by emphasising the need for HEIs to prioritise internationalisation and incorporate global learning strategies to meet the demands of the 21st century and the Fourth Industrial Revolution (4IR).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".