A Critical Review of Theoretical Models on Institutional Internationalization of Higher Education
Bibliographic record
Abstract
This paper presents a critical review of theoretical models on institutional internationalization in higher education. Over the past three decades, scholars have proposed numerous frameworks to understand internationalization processes, yet gaps persist in their practical application and integration. Through a systematic and evaluative examination of the literature, this study identifies two main categories of models: content-based models, which focus on institutional strategies and policies, and process-oriented models, which emphasize internationalization as a dynamic, cyclical progression. The critical review highlights the strengths, limitations, and developmental trends of major existing frameworks, underscoring the need for a more integrated and actionable approach. To address these gaps, we propose Qiu’s blended evidence-based theoretical model, which combines Van Dijk and Meijer’s internationalization cube with Rogers’ diffusion of innovation theory. This integrative framework provides a structured yet adaptable pathway for institutions, aligned policy, support systems, and implementation strategies to facilitate purposeful and measurable internationalization efforts. By bridging theoretical insights with practical applications, this study contributes to a deeper understanding of internationalization as a process offering valuable guidance for policymakers, researchers, and practitioners.
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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".