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
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.
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) | 0.001 | 0.000 |
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