Bibliographic record
Abstract
Globalisation is redefining the boundaries of higher education. All common and public services have been transformed into market commodities. Like any commodity in the market, education can be traded, bought, and sold. Nations are competing to emerge as global hubs of higher education. The developed first-world (OECD) countries have an enormous edge over the developing third-world countries. The developed north constitutes the core and developing global south constitutes the periphery. In this market-generated core-periphery paradigm of Immanuel Wallerstein, resources are getting transferred from poor global south to prosperous developed north along with student movement. The US, Canada, Australia, EU countries are leading destinations of students for higher education. China, India, South Korea, Arab Countries, and African Countries see top student emigration for higher education due to poor quality of education at home-state. In 2017, according to UNESCO, 5.09 million crossed borders for higher education. India sends more than 3,00,000 students abroad every year for higher education. More than one million Indian students are studying abroad, whereas less than 50,000 foreign students are studying in India. The same imbalance can be observed in all third-world countries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.251 | 0.135 |
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".