Internationalization & Career-focused Programming for International Students: A Qualitative Study of Universities in Canada
Bibliographic record
Abstract
The past two decades have seen a rapid increase in the number of international students attending higher education institutions (HEI’s) in countries around the world. The relationship between HEIs, governments, and students are often symbiotic in nature; students consider further education as a platform for career development, HEIs pursue the diversification of income and increased prestige/rankings, while governments’ attention is dedicated to the retention of skilled labour that will support and sustain national economies. Although policy makers promote the position that international students are ideal or “designer” immigrants with their education, language abilities, and cultural experiences, there are indications that international students face a number of challenges in transitioning from studies to career. Recent surveys show that 60 percent of international student respondents indicated that they plan to apply for permanent residency (PR) in Canada after graduation, combined with the increased importance of employability outcomes for international students. Given the importance of employability outcomes for students, this qualitative mixed-methods research examined the career-focused programming provided at Canadian universities. The main research question that guided this study was: What approaches are universities in Canada taking to support international students in the development of professional or career-focused skills? Participants were managers and directors from 7 universities across Canada. Three methods were used to collect data for this study: surveys, semi-structured interviews and document analysis. Three key discourses from the interviews emerged: university as a steppingstone for immigration; universities as a source of international student support; and universities as connectors to employers and regions. Research findings indicate that various factors influence career-focused programming for international students including location, size of the institution and other contextual factors. Innovative and engaging career-focused programming, with an intercultural lens provides an excellent leadership opportunity for universities to support their international student population, while simultaneously developing the competencies of domestic students to work in an increasingly global environment.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.029 | 0.012 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".