Down and out in Dhaka: understanding land financialization and displacement in austerity urbanism
Bibliographic record
Abstract
This paper examines how high levels of eviction and displacement of the urban poor have arisen due to the financialization of land in Dhaka, Bangladesh. Focusing on the Meradia neighborhood of Dhaka, it employs a historical materialist framework to explore the ramifications of the financialization of land and resulting land grabbing. It is argued that the historical transformation of land into fictitious capital in Dhaka has led to heightened displacements and evictions of the urban poor, which have occurred largely through processes of land grabbing by land and real estate development corporations in Bangladesh. Further, it is argued that the municipal and national states play key roles in the displacement of the working poor in Dhaka by facilitating private land development, failing to enforce existing rules on land grabbing, and by having a paucity of urban housing policies and plans for the urban poor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".