MétaCan
Menu
Back to cohort
Record W4401501996 · doi:10.1093/jeg/lbae026

Beyond “buzz”: knowledge interactions, innovation, and neighborhood characteristics

2024· article· en· W4401501996 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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Economic Geography · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsMcGill UniversityHEC Montréal
FundersHEC Montréal
KeywordsMarketing buzzResidenceCensusEconomic geographyBusinessCluster analysisSurvey data collectionKnowledge managementMarketingRegional scienceGeographyDemographic economicsAdvertisingEconomicsSociologyComputer scienceStatisticsPopulationMathematicsDemography

Abstract

fetched live from OpenAlex

Abstract We examine the link between neighborhood characteristics, the importance of knowledge exchange, and firm innovation in Montreal. To this end we combine two sources of data: place-of-residence census data from Statistics Canada and the results of an original firm survey. Through principal component analysis and subsequent clustering, we define five types of neighborhoods. The results revealed that firms assign higher importance to local knowledge exchange when located in dense, walkable neighborhoods with higher educated residents. Knowledge exchange, both local and global, correlates with incremental and radical innovation. Moreover, firms are innovative in any neighborhood, provided they engage in knowledge exchange.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.305
Threshold uncertainty score0.702

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
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 head0.017
GPT teacher head0.236
Teacher spread0.219 · 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