Platform Urbanism and “Splintering Amenitization”: An Analysis of Canadian Cities
Bibliographic record
Abstract
This article engages with the spatialities of platform urbanism by foregrounding where digital platforms are located in cities. Drawing on a geocoded data set of visible, material traces of platformization collected across neighborhoods in Toronto, Vancouver, and Montreal, we consider the influences that characteristics of urban built environments—including existing amenities, urban morphology, and area-level socioeconomic factors—have on platforms’ locations. Through a Poisson regression of these variables, we find that the presence of existing urban amenities most strongly explains the locations of material traces of urban platformization on the cityscape at the city block scale. We position platforms themselves as a novel amenity class that extends emplaced utility and lifestyle functions to urban residents. In so doing, we contend that the platformization of urban landscapes constitutes a form of “splintering amenitization,” wherein platformized urban amenities demonstrate spatial patterns of colocating with other, existing urban amenities in already amenity-rich areas to the exclusion of amenity-poor enclaves. This, we argue, is important because neighborhoods’ abilities to attract amenities are central to how enclaves both position themselves and compete for status within urban spatial hierarchies.
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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 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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 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".