Should Polytechnics Rely Solely on International Agents to Recruit International Students? A Case Study in New Zealand
Bibliographic record
Abstract
The aim of this paper is to investigate whether polytechnics should rely solely on international agents to recruit international students or not. To analyse the use of agents to recruit international students by tertiary institutions in New Zealand, this study attempts to find effectiveness and shortcomings of using international agents as a marketing strategy in recruiting international students. Though marketing strategies of marketing higher education is not new to the Anglo-Saxon countries (UK, US, and Australia), however, such marketing strategies of tertiary institutions in New Zealand have not been examined empirically. Deductive method of research was adopted and a total of 150 international students who studied at undergraduate and postgraduate level in a polytechnic in the Bay of Plenty in New Zealand participated in the research. The empirical findings of the research suggest that most international students relied on international agents for visa application and for enrolment process. A significant concern that a large proportion of the international students pointed to is the information mismatch between the promises by the agents and the reality that the international students faced here in New Zealand. It is suggested that relying solely on international agents to recruit international students might not be a sustainable marketing strategy for the tertiary institutions and other marketing strategies needs to be explored as well. Several policy implications in marketing strategy of higher education are also suggested in this study.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".