Cluster genesis technology-based industrial development
Bibliographic record
Abstract
Clusters - regional concentrations of related firms and organizations - are seen as being an important element of economic growth and innovation. But there is little understanding of how clusters come into existence, and little guidance provided on the role of policies that are conducive to the formation of clusters. Cluster Genesis focuses on these early origins of clusters. The case histories of well-known, established clusters, as well as more recently-developed clusters are discussed, including: DT The Hollywood motion picture cluster, DT Silicon Valley, DT Boston and San Francisco biotech regions, DT The Biotech industry in China, DT Medicon Valley in Scandinavia, DT The Irish ITC sector. Leading scholars contribute chapters examining cluster genesis, the divergent processes by which clusters arise, how multinationals contribute to cluster development, and how economic development policy may promote or hinder cluster genesis. Cluster Genesis uses a variety of methodological perspectives, examines a range of policy options, and draws on a number of rich case histories, and will be key reading for academics, researchers, and students of Economics, Innovation, Sociology, Geography, and Management Studies, as well as economic development officials and policy makers. Contributors to this volume - Frank Barry, University College Dublin David Wolfe, Centre for International Studies, University of Toronto Mario Maggioni, DISEIS (Dept. of International Economics, Institutions and Development) and Faculty of Political Science, Catholic University of Milan Luigi Orsenigo, University of Brescia and Cespri, Bocconi University, Milan, Italy Elaine Romanelli, McDonough School of Business Georgetown University Allen Scott, Department of Policy Studies and Department of Geography, UCLA Martin Kenny, Department of Human and Community Development University of California, Davis & Senior Project Director Berkeley Roundtable on the International Economy Morris Teubal, Economics, the Hebrew University, Jerusalem Martha Prevezer, School of Business and Management, Queen Mary College, University of London. Meric Gertler, Centre for International Studies, University of Toronto Maryann Feldman, Rotman School of Management University of Toronto Bo Carlsson, Weatherhead School of Management Case Western Reserve University Jason Owen-Smith, University of Michigan Walter Powell, Stanford University & Santa Fe Institute
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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 | 0.001 | 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 | 0.001 | 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".