HEALTH INSURANCE FOR INTERNATIONAL STUDENTS: WHAT HAPPENS IN NEW ZEALAND AND AUSTRALIA?
Bibliographic record
Abstract
Internationalization of education has been growing fast since the last decade where the countries such as USA, UK, Canada, Ireland, Australia and New Zealand became important study destinations in the global market. Traditionally, cost of education international students used to consider was the sum of tuition fee and living cost. However, insurance cost has been added as another component of education cost. Insurance cost is compulsory for international students to pay just like tuition fee. However, the fact is that the amount of premium payable and the benefits receivable varies from country to country. For example, New Zealand and Australia, are education exporters, and belong to similar education culture, however the costs and benefits of insurance for international students are different in both countries. Current literature focuses their analysis of the cost and benefits of insurance premium on international students mostly in individual countries. A comparative picture in this regard may contribute to make informed decision for all beneficiaries. This paper aims to compare the insurance cost of these two countries and to see how this difference has been impacting international students to choose study destination. Data from secondary sources such as government publications, statistical departments, research reports etc. of New Zealand and Australia were utilised. The paper found that the cost of insurance for international students in Australia is a little bit higher than that of New Zealand whereas the benefits of insurance received by international students in New Zealand is higher than that of Australia. However, although insurance cost is different, it has little impact on the students’ decision to choose Australia or New Zealand as a study destination. Findings of the study might be beneficial for policy makers, educationists and researchers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".