The Global University: Past, Present, and Future Perspectives
Bibliographic record
Abstract
Acknowledgements Introduction: The Global University: Past, Present, and Future Perspectives A.R.Nelson PART I: REGIONALISM(S) AND GLOBAL HIGHER EDUCATION REFORM Global Aspirations and Strategizing for World-Class Status: New Modes of Higher-education governance and the Emergence of Regulatory Regionalism in East Asia K.H.Mok Contributing to the Southeast Asian Knowledge Economy? Australian Offshore Campuses in Malaysia and Vietnam A.R.Welch PART II: THE CHANGING DIMENSIONS OF UNIVERSITY GOVERNANCE Collegiality and Hierarchy: Coordinating principles in higher education I.Bleiklie The Twenty-First Century University: Dilemmas of Leadership and Organizational Futures R.Deem PART III: ACADEMIC ROLES AND THE PURPOSES OF THE UNIVERSITY Medieval Universities and Aspirations to Universal Significance I.P.Wei The Changing Role of the Academic: Historical and Comparative Perspectives X.Xiaozhou & X.Shan PART IV: SHIFTING PATTERNS IN GRADUATE AND UNDERGRADUATE EDUCATION Toward General Education in the Global University: The Chinese Model Doctoral Education and the Global University: Studen Mobility, Hierarchy, and Canadian Government Policy, G.A.Jones & B.Gopaul PART V: UNIVERSITIES AND EXTERNAL FUNDING What Can Modern Universities Learn from the Past? English Universities Working with Industry, 1870-1914 J.Taylor Universities and the Effects of External Funding: Sub-Saharan Africa and the Nordic Countries P.Maassen Conclusion: Lessons from the Past, Considerations for the Future A.R.Nelson & I.P.Wei Contributor Biographies List of Tables and Figures
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 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".