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Record W4237469753 · doi:10.1080/14479338.2016.1265054

Innovation, creativity and governance: Social dynamics of economic performance in city-regions

2016· article· en· W4237469753 on OpenAlexafffund
David A. Wolfe, Allison Bramwell

Bibliographic record

VenueInnovation · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCreativityCorporate governanceEconomic geographyRelevance (law)Urban economicsEconomic systemKnowledge economyCreative CitiesFace (sociological concept)Social dynamicsService (business)BusinessEconomicsEconomyPolitical scienceSociologySocial scienceManagement

Abstract

fetched live from OpenAlex

The pressure towards a globalizing, knowledge-based economy raises questions about the underlying determinants of economic performance in city regions. The creation and diffusion of new knowledge drives innovation in knowledge-intensive production and service activities, which in turn, drives economic performance and growth. Although these processes are strongly shaped by national institutions and global knowledge flows, recent analyses of innovation and creativity emphasize the continuing relevance of regions in general and urban regions in particular as critical sites for determining economic performance. This work also suggests that the underlying social dynamics of urban regions are particularly significant in shaping economic outcomes. This paper explores some recent evidence on the social nature of innovation dynamics in urban regions, the increasing significance of talent and creativity in urban economies and their implications for the economic performance of city regions. It concludes with a discussion of the need for the strategic management of urban economies to cope with the challenges they face.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.006
Scholarly communication0.0060.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.048
GPT teacher head0.299
Teacher spread0.251 · 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 designObservational
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

Citations20
Published2016
Admission routes2
Has abstractyes

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