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Record W1995093763 · doi:10.1093/jeg/lbn042

Spatial diversity in invention: evidence from the early R&D labs

2008· article· en· W1995093763 on OpenAlexaboutno aff
Tom Nicholas

Bibliographic record

VenueJournal of Economic Geography · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic geographyDiversity (politics)Formative assessmentQuarter (Canadian coin)Quality (philosophy)Capital (architecture)Industrial organizationBusinessGeographySociology

Abstract

fetched live from OpenAlex

This article uses historical data on inventor and firm research and development (R&D) lab locations to examine the technological and geographic structure of corporate knowledge capital accumulation during a formative period in the organization of United States innovation. Despite the localization of inventive activity around the labs, one-quarter of inventors lived outside a 30 mile commuting radius of the nearest facility of the firm they assigned their patents to. A strong positive effect of distance from a lab on technological importance is identified, especially for inventors from large cities that were geographically separated from a firm's; labs. A patent case–control method helps explain spatial sourcing by showing that the average quality of externally available inventions was high. Firms selected complementary, not substitute, inventions from non-lab urban locations, suggesting a link between the organization and the geography of innovation.

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)0.019
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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.006
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0000.000
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 head0.066
GPT teacher head0.234
Teacher spread0.168 · 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

Citations39
Published2008
Admission routes1
Has abstractyes

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