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Record W2404919534 · doi:10.4337/9781848445079.00033

Silicon Somewhere: Is There a Need for Cluster Policy?

2008· article· en· W2404919534 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEdward Elgar Publishing eBooks · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSilicon valleyEnthusiasmVenture capitalEntrepreneurshipPolitical scienceOpenness to experienceManagementEngineeringEconomic historyHistoryLawEconomics

Abstract

fetched live from OpenAlex

All over the globe, authorities in charge of cluster policy are trying to build their own 'Silicon Somewhere' in an attempt to emulate Silicon Valley, the world's most famous example of geographical clustering of economic activity of the last three decades (Saxenian 1994; O'Mara 2004). For long, this area of South San Francisco Bay around Santa Clara County and its main cities, San Jose and Palo Alto, was mostly known for its orchards. In 1891, however, Leland Stanford founded Stanford University, which, under the leadership of Frederick Terman (1900-1982), became one of the best engineering institutions in the United States. Stanford's electrical engineering department in particular became a breeding place for innovative companies. One of these spin-offs was established by Stanford classmates Bill Hewlett and Dave Packard, who developed numerous electronic devices. Why Silicon Valley has grown into a hot spot of clustering has been examined in many studies (Saxenian 1994; Bouwman and Hulsink 2000; O'Mara 2004). The success of Silicon Valley can be largely explained by the right entrepreneurial decisions at the right place at the right moment. Stanford University, for example, benefited from Cold War federal defensive spending and the availability of venture capital. Besides this, more than elsewhere in the world Silicon Valley is supposed to have a favourable climate for talent, entrepreneurship, collaboration and innovation which has its roots in unique regional conventions such as openness to newcomers, enthusiasm for technological change, an obsession with new ideas, risk-seeking, tolerance of failure, job mobility and re-investment in the community. Nobody planned the emergence of Silicon Valley. Ever since its emergence, however, especially the Valley's micro-electronics cluster has developed and produced semiconductors and computer chips that are sold world-wide. Dazzled by this success story of clustering, many officials have paid 'policy visits' to watch the Silicon miracle. Ironically, one of the first 'policy tourists' was Nikita Khrushchev in the late 1950s, who decided that Soviet-Russia should also have its own Silicon Valley. Accordingly, he built Akademgorodok, the 'City of Science', in the middle of the taiga of Siberia. This government-planned cluster, however, failed to produce the favourable economic Silicon Valley-effect the Soviets had hoped for (Josephson 1997). In Krushschev's footsteps, public officials have done their best to transplant the phenomenon of clustering observed in Silicon Valley. In fact, they frankly admit that their goal is to copy the Californian clustering success. Regions marketing themselves as 'Silicon' or 'Valley' abound (Bouwman and Hulsink 2000; O'Mara 2004). Among the many examples of the 'Silicon Somewheres' branded within the framework of cluster policy are Silicon Alley (Manhattan-New York), Silicon Snowbank (Minneapolis-St.Paul-Area), Silicon Desert (Phoenix), Silicon Mountain (Colorado Springs), Silicon Prairie (Champaign-Urbana), and Silicon Dominion (Virginia). Apparently, high-tech clustering in the field of information technology provides the public excitement and is something with which policymakers hope to boost the competitiveness of an area. Against this background, the present paper examines the link between successful geo-economic clustering on the one hand and cluster policy on the other. The chapter aims to address problems policymakers encounter all the time, especially as they try to move towards more effective forms of cluster policy in new areas. Is there a role for government, if any, in cluster formation and support? And does it make sense to differentiate in this respect between policy for high-tech clusters and policy for low-tech clusters? In other words, is it possible to build the next Silicon Valley with the help of public policy or should policymakers stick to 'old economy'-clustering? In exploring these issues we make use of theoretical insights and anecdotal evidence regarding clusters and cluster policy. The fundamental idea of this paper is that government is not and cannot be the source of successful clustering. While clustering is valuable to the economy, governments do not have access to the knowledge that would enable them to promote the successful development of clusters. We view this epistemic problem bureaucrats face as insurmountable; if anything, it puts a clear limit on the capacity of government to create clusters. Given the fact that governments always want to facilitate clustering anyway, we present case examples of successful clusters in which government played no role or only a limited one in the field of cluster branding. Without exception, these examples show how important it is to take into account the particularities of an area. The chapter concludes with advice for policymakers to move away from their beloved 'Silicon Somewhere' to embrace a more humble approach.

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.

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)0.001
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.532
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.047
GPT teacher head0.306
Teacher spread0.259 · 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