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Record W4402655888 · doi:10.1111/jors.12734

Network versus spatial proximity and firm innovation: The case of the R&D service sector

2024· article· en· W4402655888 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

VenueJournal of Regional Science · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsTertiary sector of the economyBusinessIndustrial organizationService (business)Economic geographyMarketingEconomics

Abstract

fetched live from OpenAlex

Abstract The paper analyzes the relationship between different types of proximities—network and spatial—in relation to innovation in the context of the R&D service industry. In doing so, it contributes to the recent debate in the literature on the effects of network connectivity versus geographical colocation. The paper uses original data from a survey of 145 R&D service establishments in Montreal (Canada) and their interactions with both local and nonlocal organizations. The findings of the paper indicate that collaborative networks (both local and nonlocal) have a stronger association with R&D service innovation than spatial proximity to R&D service organizations and other collaborators. However, when these two dimensions are interacted, they are shown to function as substitutes. The paper also demonstrates that the relationship between spatial proximity and networking varies across three dimensions: local versus nonlocal networking, the type of relationship (client, supplier, competitor, and research institutes and university), and the type of network connectivity—brokerage versus closure.

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)0.002
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 score0.501
Threshold uncertainty score0.206

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.077
GPT teacher head0.257
Teacher spread0.180 · 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