How open innovation processes vary between urban and remote environments: slow innovators, market-sourced information and frequency of interaction
Bibliographic record
Abstract
Geographic research on firm-level innovation is generally premised on the idea of open innovation, suggesting that innovation occurs more readily in urban settings or clusters, which generate local buzz and allow access to external actors. However, a growing body of evidence demonstrates that firms also introduce first-to-market innovations in remote locations. In this exploratory paper, building upon work by Philip McCann, we outline a conceptual framework that connects innovators (differentiated by information source and frequency of interaction with interlocutors) and location (distance from a metropolitan area): slow innovators, relying on non-market-sourced information and infrequent contacts, will be overrepresented in isolated locations. Fast innovators, relying on market-sourced information and frequent interactions, will locate closer to cities. Our results confirm this. Our interpretation of these results – slow innovators are more reliant on technological information which loses value more slowly than faster decaying market-oriented information – requires further investigation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".