The age of knowledge : the dynamics of universities, knowledge and society
Bibliographic record
Abstract
List of Tables and Figures Acknowledgements Notes on Contributors Introduction: The Dynamics of Universities, Knowledge and Societies, James Dzisah and Henry Etzkowitz PART I: KNOWLEDGE, GLOBALIZATION AND IDENTITY 1. Normative Change in Science and the Birth of the Triple Helix, Henry Etzkowitz 2. Globalization and Scientific Research in Japan, Zaheer Baber 3. Triple Helix or Triple Jeopardy? Universities and the Social Relations of Knowledge, Terry Wotherspoon 4. The Big Shift: Science and Universities in Crisis, Toby E. Huff 5. Societal Rationalization: Cultural Innovation and Knowledge Islamization in Malaysia, Choon-Lee Chai 6. Gender and Identity in a Globalized World, Patience Elabor- Idemudia PART II: KNOWLEDGE INNOVATION, GOVERNANCE AND POLICY 7. The Triple Helix of Knowledge, James Dzisah and Henry Etzkowitz 8. Crossing Boundaries: Creating, Transferring & Using Knowledge, Harley D. Dickinson 9. Governing Innovation in a Knowledge Society, Peter W. B. Phillips 10. Public Policy Actors and the Knowledge-Based Social Order, Michael W. Kpessa 11. Regionalized Health Care System in Canada: Towards a Knowledge Management Strategy, William Boateng PART III: UNIVERSITIES, INTERMEDIATE ACTORS AND THE KNOWLEDGE ECONOMY 12. Facilitating Knowledge Transfer: The Role of Intermediating Organizations, Amy S. Metcalfe 13. Ideals and Contradictions in Knowledge Capitalization, James Dzisah 14. In the Grey Area: University Research and Commercial Activity-The Case of Language Technology, Tarja Knuuttila 15. Public Universities and Emerging Fuel Cell Technology: Insights from Singapore and Malaysia, Zeeda F. Mohamad Index
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.017 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.086 | 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".