The diverse processes behind neighborhood-level business agglomeration: the cases of Mile-End and Chabanel in Montreal
Bibliographic record
Abstract
Recent studies in economic geography observe neighborhood-level clustering of similar businesses and present it as evidence that neighborhoods are key loci of interaction and information exchange. Case studies of specific industries show that this <i>can</i> occur, but are not sufficient to infer that business interactions necessarily accompany clustering: when neighborhoods have been theorized, their nature, and the degree to which local actors interact, are shown to be complex. In this paper we identify three recurrent dimensions – industry-mix, institutional infrastructure and morphology – evoked by economic geographers, and use them to structure our exploration of two Montreal neighborhoods, Chabanel and Mile-End. Whilst some businesses in each neighborhood interact, others locate there solely for reasons of image and accessibility: local clustering does not imply local interaction. Local institutions facilitate networking activities and, in some neighborhoods, can contribute to revitalization efforts. The materiality of neighborhoods – their buildings, physical limits, location, and amenities – can support interaction, but does not determine behavior.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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 teacher head, 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".