Localized processes of platformization: The example of Surabaya
Bibliographic record
Abstract
This article analyzes how universalist paradigms for platform urbanism are being adapted, modulated, and subverted through an evolving platform ecosystem that is specific to the city of Surabaya, Indonesia's second largest city. We examine how processes of urban planning and city management are platformized, how specific groups of professionals and residents act as intermediaries between infrastructure and users and thereby facilitate the platformization process, and how these local iterations of platforms are informed by place-specific colonial and national history. By describing and tracing the genealogy of Surabaya's platform ecosystem, we demonstrate the specific ways in which it rationalizes city governance, shapes discourses on participatory citizenship and spatial planning, and redefine what counts as city infrastructure, innovation, and urban life in general. We argue that the modulation, adaptation, and resistance to platformization can only be understood by paying attention to the singularity of the milieu and tracing how visions of modernity and its sociotechnical assemblages are composed anew every time platform frameworks, tech tools, and discourses hit the ground.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".