Service Delivery To Informal Settlements In South Asia's Mega Cities: the Role Of State and Non-State Actors
Bibliographic record
Abstract
This interdisciplinary research project compares service delivery outcomes to informal settlements in South Asia’s largest urban centres: Dhaka, Karachi and Mumbai. These mega cities have been overwhelmed by increasing demands on limited service delivery capacity as growing clusters of informal settlements, home to significant numbers of informal sector workers, struggle to obtain basic services. In the absence of state supports, some informal settlements engage non‐state actors to obtain services. In order to compare service delivery outcomes through these actors, I used a layered, mixed methods approach guided by an interdisciplinary literature review and policy examination. I drew on semi‐ structured interviews as well as pairs of case studies to measure successful and unsuccessful service delivery outcomes in each of the three mega cities. Key findings are that chronic disconnections exists in all three countries, where upper tiers of the state persistently fail to create an enabling environment for lower tier state actors and municipal service delivery machinery. The cause of these disconnections is the persistent colonial impression on the bureaucracy, Neoliberal policies and the appropriation of public resources by organized crime and their backers, urban elites. Non‐state actors have facilitated service delivery to informal settlements, resulting in isolated success and improved levels of human development. However, the case studies demonstrate that the success of non‐state actors is attributed to support from lower tier state actors. A complex political economy of upper and lower tier actors, rooted in unresolved land ownership and elite interests is disabling the capabilities of lower tier state actors to extend services to the urban poor. The study informs our understanding of the role played by technical non‐governmental organizations (NGOs) in facilitating representative community‐based organizations (CBOs) engagement of state service delivery providers. The study illustrates the differential attitudes between upper and lower tier state actors towards informal settlements. The study also separates the ‘development industry’ from grass root representatives of informal settlements. The study also affirms the ability of informal settlements to organize, mobilize and engage municipal service delivery providers. The study emphasizes the need to remove constraints that upper tiers of state and society place on informal settlements in order for equitable development and sustainable levels of service delivery to be realized
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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 | 0.001 | 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 | 0.001 |
| 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".