Organizing Student Mobility: Education Agents and Student Migration to New Zealand
Bibliographic record
Abstract
The movement of international students represents an increasing component of contemporary population mobilities. Like other forms of migration, international student mobility takes place through a complex assemblage of actors and networks, including origin and destination states, educational institutions, families, friends and communities, and of course students themselves. In the midst of these arrangements education agents appear to occupy a pivotal position, serving as a bridge between student origins and study destinations in a manner that enables multiple movements across educational and geographic divides. Establishing and maintaining this important position in international student mobilities is a complex endeavour that requires agents to bridge the gap between a solely profit-oriented education industry and the social lives of students and their families. This paper investigates the position of agents in student mobilities by focusing on the development of export education activities since the early 1990s in New Zealand and the changing relationships of agents with the state, education providers and students. I trace the emergence of agents to the early liberalization of student mobility and educational provision but also note how agents became increasingly incorporated into a more formalized education industry as later governments engaged in more direct intervention and regulation of student flows and educational quality. To broaden this general overview of the role of agents the paper focuses on the specific activities and relationships of agents involved in the movement of South Korean international students. The paper concludes by highlighting the need for research on agents and other intermediaries to focus in more detail on the manner in which these actors mediate different sorts of relationships, between migration/education industries and migrant/student social networks as well as between changing state liberalization and intervention and emerging industry formations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".