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Cambridge Spurs High-Tech Growth

2004· article· en· W1604581965 sur OpenAlex

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Notice bibliographique

RevueResearch-Technology Management · 2004
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueRegional Development and Policy
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPhenomenonGovernment (linguistics)BoomPopulationVenture capitalAgrarian societyHigh techBusinessManagementEconomic growthMarketingEconomyEngineeringEconomicsPolitical scienceGeographySociologyFinanceLaw
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

During the dotcom boom, when technology innovation appeared to offer a solution to almost any structural economic woe, government technology strategists from as far away as Brazil, Canada, China and Singapore made a pilgrimage to the fenlands in the southeast of England. They travelled in the hope that if they could just understand the they would be able to recreate its success in emerging technology hubs elsewhere. It turned out that Cambridge wasn't about to hand out a recipe for recreating its Phenomenon. Being able to describe how this formerly agrarian economy, centered on a 795-year-old university, has become a hotbed of technological innovation does not mean it is easy to replicate. High-Tech Cluster Economy So what is the Phenomenon? Simply put, Cambridge and its surrounding area has become a high-technology cluster economy focused on computer hardware and software, scientific instruments, electronics and biotechnology. Academics, entrepreneurs, business and support services have coalesced to create an environment that encourages and enables the formation and growth of high-technology companies. The cluster is sufficiently well-established for serial entrepreneurs to have emerged, mentoring schemes to have been formed, and enablers such as venture capital and professional services to have flocked to the region in support. Links between firms, the university and research organizations are strong and often based on personal relationships. The impact of the Cambridge Phenomenon is evident from the statistics. According to Cambridgeshire County Council, the region's population grew by more than 20 percent in the 20 years from 1981 to 2001. By then, the region was employing 48,300 people in 1,526 high-technology companies, out of a working population of 337,510. More than half (58%) of these businesses employed ten people or less. And the focus on the city was strong, with 34 percent of the jobs based in the city and an additional 39 percent in nearby south Cambridgeshire. Explaining the Phenomenon One obvious reason for Cambridge's success is its university, one of the oldest and best-known in the world, with a record of producing more than 60 Nobel Prize winners. The University's reputation has encouraged undergraduates to stay on to do their research, and this, coupled with a collegiate organization, has tended to create strong personal networks. The colleges have responded to the entrepreneurial culture by creating science parks and business incubators. In 1970, Trinity College founded Cambridge Science Park, on land it had owned since 1546, as a response to a government report the previous year calling for better links between academia and industry. The site hosts start-ups, developing companies, technology consultancies, and attendant services such as venture capital. In 1987, St John's College built an Innovation Centre to host early-stage companies, providing business advice and help with finding funding. It has since acted as midwife to at least one billion-pound company. Networking lies at the heart of the Cambridge Phenomenon, with the colleges, departments, innovation center, and science parks all creating pools of shared experience and personal contacts. In 1998, a group of six companies formed the Cambridge Network to formalize and strengthen this networking, which has underpinned much of the region's technology development. The Cambridge Network has grown to 1,300 companies that use its website and meetings to make contacts, find services and promote their businesses. Between 100 and 200 of these are overseas companies with at least one person in Cambridge, according to Peter Hewkin, chief executive of the Network. He estimates that between a quarter and a third of these are U.S. companies. Another 20 to 30 overseas companies are members despite not having a local presence. Individuals also play a vital role in creating networks. …

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)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,705
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,002

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 Opus0,061
Tête enseignante GPT0,401
Écart entre enseignants0,341 · 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