The Geography of Intrametropolitan KIBS Innovation: Distinguishing Agglomeration Economies from Innovation Dynamics
Why this work is in the frame
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Bibliographic record
Abstract
Much has been written about innovation, territory, knowledge spill-overs and agglomeration economies, but neighbourhood-level processes of innovation have rarely been studied in a systematic fashion. This article explores whether knowledge-intensive business services (KIBS) are systematically more innovative when they are located in employment clusters. In doing so, it distinguishes between the simple co-location of innovative firms with other activities, and possible dynamic effects (identified by controlling for firm-level innovation factors): most identified geographical patterns are resilient to controls, but the geography of innovation is not straightforward. In Montreal, whilst certain types of innovation occur in employment clusters, others display no spatial patterns. Furthermore, the most intensive KIBS innovators tend to locate away from high-employment and from high-KIBS zones. KIBS innovation does not behave as expected if innovation dynamics were localised in a fashion similar to agglomeration economies: it is therefore important to distinguish between the two.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it