Composing Urban Orders from Rubbish Electronics: Cityness and the Site Multiple
Bibliographic record
Abstract
Abstract What do cities look like when rubbish electronics are the vehicle with which they are explored? This article is an experiment designed to offer a response to that question, and in doing so to productively intervene in the conversation about ‘cityness', ‘metrocentricity' and ‘subaltern urbanism'. We intervene by following flows of rubbish electronics and the action that enacts them as waste and value, drawing on fieldwork in Dhaka, Singapore, Accra and Canada's Greater Golden Horseshoe. Our intervention is an experiment in writing an urban geography of rubbish electronics as a site multiple. We show how following the circulation of rubbish electronics offers a manyfolded synopsis of cities: urban enclaves of high finance and the information economy are also industrial waste producers. Peri‐urban industrial zones are also managers of brands, legal liability and corporate public relations. Cities off the map are also urban innovation systems, while waste is rekindled as value and accumulated as poison. Thereby we suggest how a sensitivity to the site multiple may be a helpful way of grappling with shifting ontology and the performativity of our research practices in urban studies.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".