高等教育政策における学生移動 : 「教育ハブ」の創出と多文化社会の変容
Bibliographic record
Abstract
The purpose of this paper is to discuss how international students' movements affect higher education policies with an aim of forming education hubs. International students' movements have become more active, massive and diversified in the process of globalization and internationalization of higher education. People in general have come to be more concerned about studying abroad through the cross-border programs, while governments are also eagerly attracting international students for human resource development. There are several types of students' movements as follows; 1) bilateral movement, 2) multilateral movement through international networking, and :3) transit movement in which students can move from one place to another. As a result, education centers have appeared or are being designed to become hubs for international students. Those hubs are classified into three types; “student hub”,“skilled workers hub”, and “knowledge /innovation hub” (Knight, 2011), which can become an important strategic factor of higher education policy in many countries. For example, Sri Lanka has just started a reform of higher education with an aim to become “the most-cost effective and quality education hub in Asia”. Malaysia has already started functioning as a “student hub”, thus attracting various international students from Asia, Middle East and African countries. It can be said that Malaysia is a transit point as the preliminary destination for students who will eventually go on to English speaking countries like USA, UK, Canada, Australia and so on to get higher degrees. However, the formation of hub by international students' movement has also caused some issues as follows; frictions between local people and newcomers, drastic cultural changes and social security problems, one of which is the typical issue of people flowing illegaly into countries. These conflicts can stimulate people's nationalism, thus bringing about controversial political debates. International students' movements can provide higher education policies with new perspectives, while it is also true that it causes political and social problems.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".