KIBS and innovation: The geographic dynamics of innovation in Quebec
Bibliographic record
Abstract
Abstract The question we address in this article concerns the possible existence of specifically geographic processes that influence the propensity of Québec City knowledge‐intensive business services (KIBS) firms to innovate. In other words, after controlling for factors of innovation that are internal to the firm, does their neighbourhood‐level environment within the Quebec census metropolitan area partly determine their propensity to innovate? More specifically, this study looks at whether proximity to certain types of economic activity —measured by their employment levels— is connected with innovation. We show that proximity effects do exist, but that these differ according to the type of innovation considered and according to the type of activity to which proximity is measured. Our results indicate that clusters including KIBS, manufacturing, and technical KIBS seem to benefit innovation. Service establishments are, however, more innovative when they are close to the centre of Quebec City, and remote from professional services and government. The nature of these results differs somewhat from those for Montreal—in particular there is no tendency for innovation to increase with the distance to the central business district (CBD). This suggests that the connection between intra‐metropolitan location and KIBS innovation is dependent on specific metropolitan context, and does not therefore reflect easily generalisable processes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".