Mapping out the Triple Helix: how institutional coordination for competitiveness is achieved in the global wine industry
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
As of 2010, the OECD countries spent over $968 billion annually on research and development (R&D), with China spending another $179 billion, Russia $32 billion and Taiwan $24 billion. Evidently, the world’s policymakers have concluded that investment in R&D is a key to their future economic growth. As globalisation takes place, and developing countries increasingly show their ability to compete in labour-intensive manufactures, the race is on to develop new innovations that will create high skill, high productivity employment. President Obama’s championing of electric cars, alternative energy research and other high technology ventures is mirrored in efforts around the global to win the innovation race. But how such efforts should be organised is very much open to debate. This paper reviews in depth perhaps the fastest growing perspective, namely the Triple Helix. In June 2013, a Google search for ‘Triple Helix innovation’ revealed 281,000 hits. A library search gave over 1300 citations in books and papers using the same terms. An international association, TripleHelix.org, organises an annual conference featuring thousands of participants from academia, government and business. All of this indicates that the Triple Helix has become one of, if not the, most widely used perspectives on innovation. However, there are some major shortcomings with the approach, in particular its applicability to policy situations. Over the course of 2009–12, we developed case studies of the wine industry in Latin America, the Middle East, Central Asia, Australia, New Zealand, Canada and several US states by mapping out Triple Helix institutions and examining their interactions through secondary analysis of the literature; primary searches for industry and policy documents and websites; a global online survey of key actors; and, in most cases, in-depth interviews with the principals of key research, policy and industry bodies. Our exercise allows us to move towards more specific policy recommendations for improving innovation and competitiveness than Triple Helix theory has allowed up to this point. In creating a more precise and analytical mapping tool for Triple Helix interaction, we can develop the present heuristic approach of the Triple Helix into an approach that can examine what is actually happening in terms of inter-institutional coordination for innovation. With more precise maps of institutional interaction as it exists, we can understand more about what types of interactions are most effective in which situations. We are able to show the utility of this approach by revealing patterns across the wine case studies which suggest how the Triple Helix can be better understood, measured and applied to concrete situations. Above all, attention to strategy developed through consensus and policy leadership, and the development of specialised and locally-adapted hybrid organisations with both formal and informal overlapping personnel and funding, appear to be the keys to ensuring a successful Triple Helix innovation system.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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