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Record W1968442066 · doi:10.1080/00343404.2013.870988

Knowledge-Intensive Business Services (KIBS) Use and User Innovation: High-Order Services, Geographic Hierarchies and Internet Use in Quebec's Manufacturing Sector

2014· article· en· W1968442066 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueRegional Studies · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsMcGill University
Fundersnot available
KeywordsBusinessOrder (exchange)The InternetGeographical distanceService (business)Industrial organizationPoint (geometry)MarketingService providerTertiary sector of the economyKnowledge managementEconomic geographyComputer scienceGeographyWorld Wide WebFinanceMathematics

Abstract

fetched live from OpenAlex

Shearmur R. and Doloreux D. Knowledge-intensive business services (KIBS) use and user innovation: high-order services, geographic hierarchies and internet use in Quebec's manufacturing sector, Regional Studies. Geographic proximity between users and suppliers of knowledge-intensive business services (KIBS) provides no advantage in terms of innovation performance. This paper first establishes that it is those KIBS most closely associated with innovation that exhibit the highest mean distance to their users. It then shows that there is no connection between distance to KIBS suppliers and propensity to innovate. These results point to a Christallerian logic whereby innovators seek out KIBS (irrespective of distance), but whereby mean distances tend to be greater between users and innovation-related KIBS suppliers (located in central places), reflecting the different geographies of manufacturing users and service suppliers.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.053
GPT teacher head0.300
Teacher spread0.247 · 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