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Buzz‐and‐Pipeline Dynamics: Towards a Knowledge‐Based Multiplier Model of Clusters

2007· article· en· W2053959253 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeography Compass · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMarketing buzzProsperityEconomic geographyRegional studiesMultiplier (economics)Merge (version control)Pipeline (software)Computer scienceSociologyKnowledge managementEpistemologyGeographyRegional scienceEconomicsRegional developmentEconomic growth

Abstract

fetched live from OpenAlex

Abstract This article critically reviews the idea that regional prosperity and growth are heavily dependent on regional industry networks. In contrast with this view, a cluster approach is presented which emphasises both the need for close local networks and strong extralocal or global linkages. The approach argues that local interaction or ‘buzz’ and interaction through translocal ‘pipelines’ create a dynamic process of learning, knowledge production and innovation that is central to understand a cluster's success. Based on a reflexive relationship between local and non‐local knowledge flows, this approach is interpreted as a knowledge‐based extension of regional multiplier models that have been intensively discussed in regional economics since the 1960s. The buzz‐and‐pipeline conception suggested here aims to overcome problems of these models by providing a microscale explanation of regional growth processes.

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.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.969
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.021
GPT teacher head0.248
Teacher spread0.227 · 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