Going the Distance: How Proximity to Metropolitan Regions Influences Small Firms’ Innovation Modes
Bibliographic record
Abstract
We question the necessity of geographic proximity for innovation policy through an investigation of innovation modes among small firms. Our cross-economy sample of 4,887 Canadian small firms enabled an examination of innovation mode use per firm location. It revealed that geographic proximity, as distance to metropolitan regions, is more critical for some innovation mode learning processes than others. Although the science-technology-innovation (STI) mode (e.g., R&D) and the external dimension of the doing-using-interacting (DUI) mode (e.g., supply chain collaborations) feature prominently in policy, small firms are less likely to engage in these activities as their distance to metropolitan regions increases. Whereas use of STI and external DUI relies on external knowledge sources, the internal activities associated with the DUI mode (e.g., cross-functional teams, employee training) remain comparatively viable for small firms in less proximal locations. For small firms far from metropolitan regions, we propose that impactful innovation policy should aim to support the internal capacities of small firms, thereby serving as a counterweight to a policy tradition dominated by STI interventions stressing external interactions. Our findings further endorse the important role of geographic contexts for innovation modes and add to the currently thin research on the DUI innovation mode.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".