Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Several decades ago, private higher education already ranked as a major force in the higher education realm in many countries. Expansion in Latin America had begun in the 1960s, and the private sector was dominant in several key East Asian nations. At that stage, the forces shaping higher education were relatively stable. Then, in the last quarter of the 20th century, the dynamics changed dramatically, and private higher education has suddenly become the fastest-growing segment of higher education worldwide-expanding rapidly in almost all parts of the world. This book helps to highlight trends and realities of private higher education around the world. We have organized the book into two sections. The first deals with international trends and issues, while the second-much longer-section focuses on countries and regions. The majorityof the book’s chapters concentrate on single countries. Authors have written from their own points of view. Some are critical of private higher education development, others express praise, whereas most offer objective observation and analysis. All are united in the belief that this phenomenon is a centrally important aspect of higher education-and one that will continue to expand.
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.
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) | 0.024 | 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 it