Recruiting international students: A comparative study at the University of South Carolina and McGill University
Bibliographic record
Abstract
This research is an attempt to capture and compare best practices in international recruitment of students. The study is a reaction to policy and finance shifts in regards to international recruitment at the university level between two large, public institutions: The University of South Carolina in Columbia, SC and McGill University in Montreal, QC. For the purpose of this study, international recruitment refers to undergraduate students with citizenship outside of the host institution's country, who wish to complete a four year degree at the university. In order to understand the breadth of this trend, the study uses a comparative case study approach specifically focused on active and passive recruitment practices at each school. The external factors in regards to campus, regional, and national culture and politics are addressed as well. Both schools recognize the benefits of recruiting from abroad; the research cites many of these including increased diversity of the student body and a strong financial incentive. A major financial push at USC and a policy shift at McGill to incentivize certain majors make the study of best practices in recruitment relevant for both schools. Kolb's experiential learning theory, promoting the need for students' engagement, serves as the foundation for the motivations behind international recruitment, demonstrating the need to prepare students for a globally-affected labor market after graduation.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.029 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".