Strategizing the for-profit city: The state, developers, and urban production in Mega Manila
Bibliographic record
Abstract
This article explores the evolving role of real estate developers in the wider metropolitan region of Manila, the Philippines. We argue that, given the relational nature of these actors, they are a relevant object of analysis for the formulation of “mid-level” theories that take into account both global, macroeconomic trends and local, history-dependent contingencies. As we consider developers’ activities and interactions with a wide range of public and private actors, we retrace their gradual empowerment since the beginning of the postcolonial period. As a handful of powerful land-owning families created real estate development companies, urban production quickly became dominated by a strong oligarchy capable of steering urban development outside the realm of public decision-making. Philippine developers subsequently strengthened their capacity by stepping into infrastructure provision, seemingly expanding their autonomy further. More recently, however, we argue that while the role of private sector actors in shaping urban and regional trajectories has scaled up, their activities have been tethered more strongly to a state-sponsored vision of change. Both by reorienting public–private partnerships (PPP) toward its regional plans, and by initiating new forms of public–private partnerships that give it more control, the state is attempting to harness the activity of developers. We characterize this shift as a move from the “privatization of planning” to the “planning of privatization” of urban space.
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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 | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".