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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 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; both teacher heads agree on what is shown here.
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".