Institutional Policies and Practices for Admitting, Assessing, and Tracking International Students
Bibliographic record
Abstract
The United States has the largest market share of international students at 22%, followed by the United Kingdom at 11% (Project Atlas, 2015). The U.S. share has decreased from 28% in 2001 although total numbers ofinternational students are increasing (Project Atlas, 2015). Decreased market share may be due to targeted national strategies in other countries to attract international students. These include immigration policies that not only expedite obtaining a student visa, but provide opportunities to work while studying and permanent jobs and residency after graduation (e.g., Canada, the Netherlands, Germany, Sweden) (Lane, 2015). Nations are also actively recruiting, providing databases with comprehensive information about studying in the country, (e.g., the Netherlands), and offering financial incentives (e.g., Germany)(Lane, 2015). In some cases, countries that once sent students to study abroad (United Arab Emirates, Singapore, Malaysia) are now actively recruiting to host students from their regions (Lane, 2015).
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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 | 0.174 | 0.222 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".