Gentrification and the an/aesthetics of digital spatial capital in Canadian “platform cities”
Bibliographic record
Abstract
This paper reports on the findings of an empirical study of the street‐level visual spatialities of urban platforms in three Canadian cities: Toronto, Vancouver, and Montreal. Enumerating, typologizing, and spatially analyzing incidences of platforms in these three cities, we find platforms to be concentrated in neighbourhoods classified as “gentrified,” “gentrifying,” and “gentrifiable,” while being largely absent from established affluent enclaves. We theorize the significance of these spatialities in three ways. First, we suggest that the emplaced visibility of platforms functions to cue expenditures of digital spatial capital—the ability to stake claims to space through engagements with digital technologies—in neighbourhoods where these platformized materialities are visually encountered. Second, we argue that these expenditures of spatial capital are associated with the ways in which platforms glamorize mundane urban consumption practices (the aestheticization of consumption) while decoupling acts of consumption from face‐to‐face interaction (the anaestheticization of social relations). And third, we identify propositions for how these an/aesthetic dynamics may potentially influence the further densification of platforms on city streets in transitioned (gentrified) and transitional (gentrifying and gentrifiable) urban enclaves .
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".