The micro-geography of knowledge exchanges in Montreal: Questioning the importance of the neighbourhood scale in an age of virtual communications
Bibliographic record
Abstract
Observation and theory confirm that economic activity can benefit from spatial agglomeration and clustering. Typically this has been analysed at the region or city scale, but recently micro-local and neighbourhood dynamics have drawn attention. Most studies first observe agglomeration, then infer or theorise processes that drive it; these inferred processes have become embedded in urban policy thinking. One such process is localised knowledge exchange, believed to be encouraged by spatial proximity and third spaces such as cafes and parks. In this study of Montreal firms, we directly explore the importance that firms attach to different scales and places at which knowledge exchange occurs. Overall, micro-local and local scales are considered less important than metropolitan and wider scales; third spaces are not considered important, except by marketing innovators; and there is no connection between innovation and the importance of local scale for knowledge acquisition. However, results are not homogeneous across urban context, economic sector or innovation profile: the association between micro-local knowledge exchange and geographical location is complex and cannot be generalised across neighbourhoods or 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".