Bibliographic record
Abstract
They examine the purpose of the university, its evolution and change, its degree of autonomy, evaluations of performance and accountability, its role in guaranteeing human rights, financing, and efficiency and the influence of technology on instruction and structure - all issues that are highly relevant to university leaders and legislators who seek to form and fashion responsive and workable institutions and systems of higher education.The authors suggest measures needed to overcome organizational inertia and recognize the necessity of responsiveness to social and economic changes. Different aspects of worldwide human rights struggles that bear on the university are discussed - for instance the situation in South Africa, where higher education institutions are seeking to redress the misdeeds of the past. The authors also address the issue of public versus private institutional competition and the emergence of the private for-profit institution. Finally, the realities of how and to what extent technology can be relied upon to improve college and university instruction is examined. Contributors include Don Aitkin (University of Canberra, Australia), F. King Alexander (University of Illinois), Kern Alexander (University of North Florida), Michael J. Beloff (Trinity College, Oxford), Ian Clark (Council of Ontario Universities), Stephen R. Greenwald (Audrey Cohen College, New York), James J. Mingle (Cornell University), John H. Moore (Grove City College, Pennsylvania), David W. Olien (University of Wisconsin System, Madison), and David R. Woods (Rhodes University, Grahamstown, South Africa).
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.042 | 0.010 |
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".