Overcoming Barriers in Higher Education Mobility between Latin America, Canada and Asia: A Scoping Review
Bibliographic record
Abstract
Latin America and Asia have been tied for hundreds of years through a transcontinental trade network, which has culminated in their current economic interdependence. This interdependence necessitates cooperation, which can be bolstered through cultural understanding between the two continents. International student mobility is one way to foster intercultural relations, which are currently quite low between these regions. Canada has faced a similar struggle as Latin America to attract students in the Americas when faced with competition from US universities, but has had some successes which Latin American countries could learn from. This study therefore completes a scoping review of the literature to categorize barriers and enablers to academic mobility between higher education institutions (HEIs) in Asia, Canada, and Latin America and synthesizes relevant suggestions. An integrative literature search of qualitative and quantitative studies was conducted using six different databases. After considering inclusion and exclusion criteria, 33 studies were selected and analyzed. The results were categorized into six themes: Cultural, Academic and Professional, Linguistic, Economic, Program Structure, and Political Climate. Each theme included factors which enabled or hindered student mobility between Asia and the Americas. The findings highlight the need for Latinamerican HEIs to emphasize relevant initiatives and qualities that go beyond rankings, boost the use of English among academics and staff, actively reach out to Asian partners, and collaborate to develop credit transfer policies compatible with Asian institutions. These considerations could be all the more timely considering students are currently more open to virtual international opportunities in the midst of the COVID-19 pandemic, generating possibilities of greater collaboration between these regions of the world.
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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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.019 | 0.034 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".