Bibliographic record
Abstract
In a climate of ongoing funding cuts for higher education, attracting international students (ISs) has become an economic essential survival strategy for most post-secondary institutions (PSIs) in Canada. Within the past two decades, hundreds of privately funded institutions have emerged particularly in the Greater Toronto Area, offering Canadian training to ISs at a relatively low cost. Within a context of minimal state regulations new and complex partnerships have evolved between various government and non-governmental actors, who work both locally and transnationally to recruit ISs. Drawing on in-depth interviews with 30 Indian ISs in GTA colleges and 15 KIs in India and Canada, we identify the involvements of these actors in the recruitment process, at various scales and stages of IS migration. Our research demonstrates that even though the roles of these actors may be identified, their responsibilities are perhaps purposefully obfuscated, in order to evade accountability. Using Agency Theory, we demonstrate that, in the context of IS recruitment, “agents” are neither well-defined entities nor positioned hierarchically within the larger system, with fixed roles and influences. Rather, they are highly dynamic and fluid, and their roles expand and contract in response to local and transnational structural differences, relationships with other agencies, and networks.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 0.017 |
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".