Higher Education: An Emerging Field of Research and Policy
Bibliographic record
Abstract
An Emerging Field of Research and Policy PHILIP G. ALTBACH Higher education has become a vast twenty-first century enterprise, central to postindustrial globalized economies everywhere. More than 100 million students study in at least 36,000 postsecondary institutions worldwide. In most countries, higher education has become a large, complex enterprise, comprising large academic systems, nonprofit and for-profit private institutions, and an array of specialized schools. As universities and other postsecondary institutions have grown, they acquire elaborate administrative structures in need of major expenditures of public and, often, private funds. Moreover, higher education has become big business. Academic institutions employ thousands of people and educate tens of thousands-or in some cases hundreds of thousands. Degrees in a multiplicity of specialties from ancient history to biotechnology are offered. In 1971 Eric Ashby characterized the American academic system as offering “any person, any study,” in describing its diversity and scope. Martin Trow analyzed the progression of higher education from elite to mass and finally to universal access (2006). In the industrialized nations, at least, mass access has been achieved, and a few countries-first the United States and Canada and, recently, South Korea, Finland, Japan, and many others-enroll upwards of 70 percent of the relevant age group. Many others, mainly in Europe and the Pacific Rim, educate half or more of the age group. Developing countries lag behind, and the main growth in the coming decades will be in this part of the world (World Bank 2000).
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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.016 | 0.012 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".