Visitors or stakeholders?: Engaging international students in the development of higher education policy
Bibliographic record
Abstract
International students are a tremendous strength to enhance the internationalization of higher education. The purpose of this study was to reveal how and to what extent international students at UPEI have been involved in policy-making processes as an approach of their meaningful engagement in an international learning environment. I researched the history, context, definition, rationales, institutional policies and strategies of internationalization of higher education, as well as the contributions, motivations, challenges, and needs of international students. The data came from institutional documentations and 10 interviews with both administrators and international students of this university. At UPEI, international education and supports to international students have been emphasized in its strategic plans, but internationalization is not clearly stated in its mission and vision statement. Administrators demonstrated strong passion in supporting international students, including involving international students’ in policy-making processes. International students reported some challenges in their lives as well as participation in the management of the university. These challenges resulted in low involvement of international students in decision-making processes at UPEI. Findings imply that a welcoming campus culture and an inclusive mechanism are needed to truly engage international students in policy-making processes. Currently, international students are visitors rather than stakeholders. To make the learning environment genuinely inclusive and to have international students’ identity represented and valued in every aspect of teaching and management are more critical in addressing internationalization of higher education than solely helping international students adapt their lives to a new environment. Internationalization should be a powerful force to promote the coexistence of different cultures, rather than eliminating the difference or melting the diversity into one unified representation.
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 | 0.021 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.009 |
| 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".