Bibliographic record
Abstract
1. Introduction by Douglas M. Priest, Edward P. St. John, and Rachel Dykstra Boon I. Public Policy and Privatization 2. State Support of Higher Education: Past, Present, and Future by Donald E. Heller 3. Privatization and Federal Funding for Higher Education by Edward P. St. John and Ontario S. Wooden 4. The Ideology of Privatization in Higher Education: A Global Perspective by Fazal Rizvi II. Generating Revenue from Alternative Sources 5. Alternative Revenue Sources by James C. Hearn 6. Students and Families as Revenue: The Impact of Institutional Behaviors by Don Hossler 7. Patents and Royalties by Joshua B. Powers 8. Philanthropy by Aaron Conley and Eugene R. Tempel III. Modernizing Public Universities 9. Incentive-Based Budgeting Systems in the Emerging Environment by Douglas M. Priest and Rachel Dykstra Boon 10. Privatization of Business and Auxiliary Functions by Douglas M. Priest, Bruce Jacobs, and Rachel Dykstra Boon 11. Enterprise Systems by Don Hossler and William Gorr 12. E-Learning by James Farmer, instructional media and magic, inc. IV. Making Sense of Change (and Finding Dollars, Too!) 13. Privatization and the Public Interest by Edward P. St. John 14. Privatization in Public Universities by Edward P. St. John and Douglas Priest References
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".