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Record W2094804149 · doi:10.1080/09654310802315385

Unity and Diversity in High-tech Growth and Renewal: Learning from Boston and Silicon Valley

2008· article· en· W2094804149 on OpenAlexaff
Henry Etzkowitz, James Dzisah

Bibliographic record

VenueEuropean Planning Studies · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSilicon valleyHigh techDiversity (politics)Economic geographyRegional developmentGovernment (linguistics)Regional scienceTrajectoryProcess (computing)EconomicsSociologyGeographyArchaeologyEntrepreneurshipComputer scienceAnthropologyFinancePhilosophy

Abstract

fetched live from OpenAlex

A new model of knowledge-based regional economic development was invented in Boston during the 1930s and subsequently transferred to northern California where it also had independent roots. Drawing upon academic, business and government resources and configuring them in new formats created new firms and new industries. Nevertheless, the two regions often appear dissimilar when they are contrasted synchronically, due to the different stages they may be in at the time. Thus, some observers argue that Boston and Silicon Valley are distinctive watersheds, irrelevant to follow-on regions. However, if the development process of these two prototypical high-tech regions are analysed diachronically, a trajectory with similar phases of development may be identified. We suggest that these two regions exemplify a general model for high-tech regional growth and renewal.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.212

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.011
Scholarly communication0.0060.005
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.065
GPT teacher head0.237
Teacher spread0.172 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2008
Admission routes1
Has abstractyes

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