Bibliographic record
Abstract
‘Platform urbanism’ has recently gained traction as a designator for emergent dynamics and material configurations associated with the increasing presence of digital platform enterprises in cities. Initial scholarly engagements with platform urbanism have tended to coalesce around critiques of digital platforms as progenitors of inevitably dystopian urban futures. In this paper, I advance a counter-topographical minor theory of platform urbanism. I do so by drawing on Legacy Russell's notion of the glitch as a tendency toward both error and erratum (correction) in digital systems, mobilizing space/times where platforms appear ‘glitchy’—unexpectedly, otherwise than anticipated, or not at all—as the margins of platform urbanism. Through the narration of three specific platform/city interfaces from the minors of their glitchy margins, I capture the ways in which platform–urban configurations are demonstrably open to negotiations, reconfigurations, and diffractions through tactical maneuvers rooted in everyday digital practices of urban denizens. Theorized from the minor, platform urbanism is a phenomenon that may beget an array of possible outcomes that remain shapeable by mundane tactical interventions in the platform-mediated present. This ultimately underwrites possibilities for more hopeful digital urban politics, theory, and futures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".