Bibliographic record
Abstract
Transnational education (TNE), interpreted as the mobility of education programs and providers between countries, has dramatically changed in scope and scale during the last decade. New actors, new partnerships, new modes of delivery, and new regulations are emerging. This has resulted in a proliferation of TNE terms and mass confusion in how they are used. The purpose of this article is to develop a common TNE framework of categories and definitions which can be used by both TNE sending and host countries. The framework needs to be robust enough to distinguish between different forms of TNE but flexible enough to be used by a wide range of institutions/countries around the world. Key elements common to twinning, franchise, joint/double/multiple degree programs as well as international branch campuses, cofounded institutions, franchise universities, and distance education are closely examined to ensure that the framework clearly differentiates between collaborative TNE and independent TNE modes of delivery. Much is at stake in terms of quality assurance, enrollment planning, policy/regulatory development, and the monitoring of trends if the proliferation and confusion among TNE terms continue. Different uses of the TNE framework are discussed, including the need for an internationally agreed-upon set of definitions as a precursor to developing an international protocol for worldwide collection of TNE data, similar to what United Nations Educational Scientific and Cultural Organization (UNESCO) and Organisation for Economic Co-Operation and Development (OECD) do for international student mobility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".