The urban under erasure: Towards a postcolonial critique of planetary urbanization
Bibliographic record
Abstract
In my engagement with the planetary urbanization thesis, I make three main interventions: (1) I emphasize the pioneering contributions that postcolonial and relational geographical approaches have made to planetary thought long before the recent planetary turn in urban studies; (2) I underscore the disconcerting ethico-political implications of planetary urbanization's will to map the “extended landscapes” of urbanization and its reduction of contemporary planetary condition to the imperatives of capitalist urbanization; and (3) I offer the deconstructive strategy of writing “under erasure” that puts both the city and urbanization under erasure to highlight the blind spots of planetary urbanization. Then to demonstrate the value of writing under erasure, I focus upon waste – as both material and semiotic artifact of capitalist urbanization – and offer a “supplementary reading” of Bangalore that sketches the multiple constitutive outsides of the city, which in turn make empirically evident the stakes of planetary urbanization's occlusions. I conclude by suggesting that proponents of planetary urbanization and urban studies more broadly embrace writing under erasure as a useful epistemological orientation to build better theories of the urban.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.084 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".