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Enregistrement W2404919534 · doi:10.4337/9781848445079.00033

Silicon Somewhere: Is There a Need for Cluster Policy?

2008· article· en· W2404919534 sur OpenAlex

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Notice bibliographique

RevueEdward Elgar Publishing eBooks · 2008
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueRegional Development and Policy
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésSilicon valleyEnthusiasmVenture capitalEntrepreneurshipPolitical scienceOpenness to experienceManagementEngineeringEconomic historyHistoryLawEconomics

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,532
Score d'incertitude au seuil0,946

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,047
Tête enseignante GPT0,306
Écart entre enseignants0,259 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle