Filipinos on education migration pathways to English-Using destination countries
Bibliographic record
Abstract
English-speaking migration destination countries have implemented policies that promote pathways for international students to transition into skilled migrants. In 2019, Australia alone recorded total international student enrolment approaching one million, underscoring the economic significance of the sector (Australian Department of Education, Skills and Employment, 2019). This has positioned international education as the destination country’s largest services export. In an increasingly competitive global market, the availability of post-study work opportunities has become a key factor influencing student recruitment and enrollment in educational institutions in destination countries. However, in Australia (Morris et al., 2021) and Canada (Pottie-Sherman et al., 2024), both top destinations for study abroad programs, temporary educational migrants are unfairly blamed and cited as contributors to domestic challenges such as housing shortages and job scarcity for locals, leading destination countries to enforce stricter regulations on these pathways. This study examines the factors that influence students' choices to pursue education in English-speaking countries, the strategies Filipinos use to navigate educational migration pathways, and the impact of language on their study-abroad choices in these destinations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".