Current Trends in Regional Innovation System Thinking and Policy Making
Bibliographic record
Abstract
The literature on the spatial organisation of innovation has been exemplified by concepts such as ‘innovative environments’ (Aydalot, 1985), ‘innovative milieux’ (Camagni, 1991), and ‘regional innovation systems’ (Cooke, 1992). Over the past two decades, the concept of regional innovation systems has evolved into an accepted way of understanding the uneven spatial development of the knowledge-based economy. Innovation systems emphasize both the importance and uniqueness of context, path-dependency and lock-ins, and complementary action by a broad range of stakeholders in affecting change. The focus on a broad set of actors (private, public, and more recently society in a broader sense) and formal and informal interactions for knowledge dissemination, adaptation, and production to explain regionally produced competitive advantage made regional innovation system thinking a popular tool for policy makers to support the creation of new pathways for exchange by means of implementing a vast array of newly developed innovation instruments (Guy, 2014; Teirlinck et al., 2013), not in the least in the field of technology intermediaries. Our understanding of regional innovation systems is not static and has evolved in line with major trends in innovation (such as open innovation - Chesbrough, 2003) but also in terms of our understanding of how to account for the specific regional context (Asheim et al., 2013). System components, linkages, and boundaries, along with path-dependency (Boschma and Frenken, 2006) and endogenous potential (following the views provided by Friedmann, 1972; and Aydalot, 1985) reflected in critical mass, absorptive capacity, smart specialisation, network capabilities, and the institutional framework within the region, are increasingly being considered keystones in regional innovation system thinking (Guy, 2014). This innovation system way of thinking is opposed to a neoclassical economic approach assuming that economic agents can easily change production technology or its underlying broader knowledge base (Guy, 2014). Building on some of the core themes presented and debated at the 8th European Conference on Innovation and Entrepreneurship (ECIE 2013, Brussels, Belgium), this special issue of the International Journal of Innovation and Regional Development is devoted to actual trends in innovation systems and policy making. The issue offers a balanced geographical mixture of regions in emerging and more developed economies (Mexico, Poland, Canada, Belgium, and Indonesia) as well as a triangulation of qualitative and quantitative approaches. The issue starts with two papers providing original overall system level insights in further strengthening our understanding of the regional innovation system approach. They capture the permanent changes - territorial, sectoral, technological - innovation system thinking is undergoing. Starting points are that each system has its own features and characteristics, and the necessity to give a dynamic description of the configuration of each system in order to forecast its possible future evolution.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".