Cross-Border Transactions in Higher Education: Philippine Competitiveness
Bibliographic record
Abstract
The international education service sector is undoubtedly growing. The movement of students across nations is expected to grow fourfold in the next quarter of a century. Undaunted by the current domination by English-speaking providers, countries in Asia have taken big steps to be centers of education in the region, an ambition. Their single-mindedness in the pursuit of this vision has already made them countries to contend with._x000D_ This paper shows that the focus and determination of countries like Singapore, Malaysia and China, is not present in the Philippine environment that is characterized_x000D_ by an unusually high dependence on the private sector to meet the growing demands for education. Marred by a highly politicized setting and inadequate resources, the education sector struggles in its aims to provide education for the growing population at an affordable rate and still maintain a decent level of quality. With these conditions, the Philippines, slowly losing its edge in English education in the region, can only hope to niche and attract foreign students and academics into specific programs and institutions, hopefully with the concerted support of government. If Government is serious in its desire to compete internationally, policymakers must address squarely the barriers to achieving this, including the enactment of laws to facilitate the influx of education services trade.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.101 | 0.011 |
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".