Public Policy and the Attraction of International Students
Bibliographic record
Abstract
In this project we asked: in this competition to attract and retain high quality students, how are the major Anglophone governments responding to and shaping the international brain race? The very recent discussion paper from the Ministry of Training, Colleges and Universities, Developing global opportunities, demonstrates the timeliness of this study for the Ontario context (MTCU, 2016). Thus the research questions underpinning the project were: 1. What is the policy framework for attracting international students in these jurisdictions? 2. How and why has this policy framework evolved over the last fifteen years (2000-2015)? 3. How competitive is Ontario in relation to other jurisdictions? 4. What policy levers could Ontario consider moving forward to enhance its position globally as a premier destination for foreign talent? The findings of the project are divided into six categories: political climate and policy framework, government initiatives, major reports, legislation, funding, and external factors. Within each category we offer two short case studies, generally drawn from a single jurisdiction, that exemplify ‘lessons learned’ that may be relevant to the Ontario setting.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 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".