Cluster-Based Economic Strategy, Facilitation Policy and the Market Process
Bibliographic record
Abstract
The geographical concentration of related manufacturing and service firms is as old as economic development, but it has drawn renewed attention in the last two decades in the wake of the spectacular growth of a number of regional economies ranging from Silicon Valley (South San Francisco Bay) to Italian rural manufacturing districts. While numerous policy prescriptions for regional growth that built on this phenomenon have been devised, none has enjoyed more popularity among policy makers than the "cluster" based economic development strategy put forward by Harvard Business School's Michael Porter. In Porter's views, clusters are made up of firms that are linked in some ways and that are geographically proximate. Upon closer examination, however, this concept turns out to be so fuzzy that it is now commonly used in a variety of ways by a wide array of academics, consultants and policy makers. It is further argued that the regional specialization strategy commonly associated with clusters makes regions more likely to experience economic downturns, prevents the spontaneous creation of inter-industry linkages and hampers the creation of new ideas and businesses.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".