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The age of knowledge : the dynamics of universities, knowledge and society

2012· book· en· W651274635 on OpenAlexaboutno aff
James Dzisah, Henry Etzkowitz

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsKnowledge societySociology of scientific knowledgePolitical scienceGlobalizationSociologyKnowledge economySocial scienceLaw

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.588
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.285
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations16
Published2012
Admission routes1
Has abstractyes

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