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Record W86494344

Aggregate evidence of localized academic knowledge transfer in the U.S

2013· article· en· W86494344 on OpenAlex
Eric T. Stuen

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEconomics bulletin · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInnovation Policy and R&D
Canadian institutionsnot available
Fundersnot available
KeywordsKnowledge spilloverMetropolitan areaSpillover effectSample (material)Panel dataQuarter (Canadian coin)Technology transferEmpirical evidenceKnowledge transferAggregate dataEconomicsAggregate (composite)Regional sciencePolitical scienceDemographic economicsMarketingBusinessSociologyEconometricsGeographyManagementIndustrial organizationMedicineMacroeconomicsInternational trade
DOInot available

Abstract

fetched live from OpenAlex

Technology transfer and, more broadly, knowledge spillover from universities to industry has become increasingly studied as universities have become charged with driving local economic growth. This study offers several empirical improvements over prior efforts to measure the aggregate local effects of academic research. It uses counts of scientific publications and citations as more direct measures of academic knowledge than R&D spending. It makes use of panel data with greater breadth and depth: the sample covers all 105 U.S. metropolitan areas with significant academic research and spans 22 years. The positive local geographic association between university research and private-sector patenting found in prior studies is reaffirmed. There is some indication that this relationship strengthened in the last quarter of the sample, 1994-1999, suggesting that academic research was becoming more important to innovation in the 1990s. However, the volume of academic research was not found to have an effect on the rate of citations received by patents.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.005

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 head0.063
GPT teacher head0.260
Teacher spread0.197 · 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