Financializing urban infrastructure? The speculative state-spaces of ‘public-public partnerships’ in Jakarta
Bibliographic record
Abstract
Mega city-regions in the global South facing challenges posed by rapid urbanization have turned to infrastructural solutions, steeped in speculative ‘global-city’ imaginaries and national developmental aspirations, in order to unclog catch-up growth. This infrastructural imperative for growth reflects a broader infrastructure fix, as creditor states and development banks with geopolitical and geoeconomic interests advance competing market-based and state-led models to finance and develop infrastructure. In Jakarta, Indonesia, I examine the coming together of these models as they articulate with the political-economies of city and state, and their path-dependent restructuring following the 1997 Asian Financial Crisis. In Jakarta's speculative state-space political interests and developmental objectives of state and city governments are entangled with the capital accumulation strategies of State-Owned Enterprises. With a number of rail transit projects in the city-region driving a boom in Transit-Oriented Development, State-Owned Enterprises speculate on market conditions and the ‘world-class city’ dreams of middle-class residents to leverage their property assets. This financial speculation is equally premised on political speculation around the planning and execution of infrastructure projects, framed by the developmental politics of affordability and accessibility to the city. I examine how these strategies, practices and tensions come together to produce innovative governance arrangements in the provision and management of transport and housing through Public-Public Partnerships.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".