The (micro) geography of collaborations and interactions in an urban context
Bibliographic record
Abstract
We examine the micro-geography of collaboration and interaction patterns in an urban context. More specifically, we investigate whether firms exhibit different collaboration and knowledge exchange patterns depending on their locations and their level of urban density. We explore this question using an original survey on R&D knowledge-intensive business services (R&D KIBS) in Montreal. The results reveal that R&D KIBS in Montreal predominantly collaborate with actors in close proximity, with the majority being located within 25 km. We also provide evidence that the propensity to collaborate with different actors and the intensity of interactions with direct collaborators is not directly associated with any intra-metropolitan patterns. Conversely, urban density is associated with other interactive forms of knowledge exchange, especially for smaller firms. This article contributes to an emerging literature that seeks to understand intra-metropolitan dynamics of knowledge exchange and innovation and how they unfold for heterogeneous actors. • R&D KIBS in Montreal collaborate predominantly with local actors to exchange knowledge. • The exact location of firms does not influence their propensity to engage in direct collaborations with other actors. • Urban density is associated with other forms of knowledge exchange such as informal contacts, especially in smaller firms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".