Platforms and/as urban communication: Mediums, content, context
Bibliographic record
Abstract
Abstract This paper brings an urban communication lens to bear on the geographies of platformisation in cities. It does so by drawing on three select instances of platformised materialities in Toronto and Vancouver that represent familiar contours of urban platformisation: mobility (bike and car sharing), last‐mile logistics (on‐demand delivery), and labour (gig work). These examples are worked through Aiello and Tosoni's heuristic of cities as constituting the mediums, content, and contexts of urban communication, respectively. As mediums, platformised materialities in the form of street signs designate exclusive uses of public space by mobility platforms, communicating the spatial conditions of platform urbanism. As the contents of communication, stickers and signs advertising on‐demand meal delivery available at a restaurant venue express the platform‐driven transformation of the social relations that make the delivered meal take place. And as context, broader trends of the platformisation of labour render communication by other, non‐platform‐based materialities – such as posters calling on urban gig workers to unionise – meaningful. An urban communication perspective contributes to geographical scholarship on platform urbanism by nuancing our understandings of how platforms and platform technology capital secure and sustain themselves in cities through their material communicative capacities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".