Globalizing Campuses: The Effectiveness of Post-Secondary International Student Recruitment
Bibliographic record
Abstract
As the world continues to become more globalized, so does education. The internationalization of higher education is inevitable with globalization and institutions continue to recruit students from around the globe to diversify their institution. The question is how institutions do this and why it matters. This paper answers these questions by uncovering the best practices of recruiting and supporting international students at post-secondary institutions in the City of Toronto and the Greater Toronto Area. In order to determine the best practices and support services interviews have been conducted with employees in the international student recruitment (ISR) industry and surveys have been provided to international students. Interviews have been analyzed to identify the ISR strategies currently in place at post-secondary institutions in Toronto, and surveys have been analyzed to identify the student perspective of these methods and the support provided to them. Both sets of responses have also been compared to identify ways to improve ISR and international student support services. This paper will uncover the ways in which ISR is conducted, the ways students perceive these methods, and how best meet student needs in the future. Based on the research conducted it has been determined that the most effective strategies for ISR are relationship development, transparency of institutional expectations, and the use of effective cross-cultural communication practices. Students have assisted in determining that institutions in the GTA do have support services in place and most do provide adequate services to students. Many recommendations have been made to improve ISR including obtaining feedback from students to incorporate student needs into ISR practices and ensuring that a clear outline of the Canadian education system is provided to students.
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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 | 0.063 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".