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Record W3121764353

Stylized Facts in the Geography of Innovation

2010· article· en· W3121764353 on OpenAlexaff
Maryann P. Feldman, Dieter F. Kogler

Bibliographic record

VenueHandbook of the economics of innovation · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsStylized factMarketing buzzEconomic geographyDiversity (politics)UrbanizationInnovation processProcess (computing)Economies of agglomerationGeographyEconomicsBusinessSociologyEconomic growthComputer science
DOInot available

Abstract

fetched live from OpenAlex

The geography of innovation describes the importance of proximity and location to innovative activity. As part of what has been termed the new economic geography, this area of research is less than 20years old, and is now developed sufficiently so that the discussion can be organized around certain stylized and commonly accepted facts:•Innovation is spatially concentrated.•Geography provides a platform to organize economic activity.•All places are not equal: urbanization, localization, and diversity.•Knowledge spillovers are geographically localized.•Knowledge spillovers are nuanced, subtle, pervasive, and not easily amenable to measurement.•Local universities are necessary but not sufficient for innovation.•Innovation benefits from local buzz and global pipelines.•Places are defined over time by an evolutionary process.

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.024
GPT teacher head0.211
Teacher spread0.187 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2010
Admission routes1
Has abstractyes

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