Full‐cost recovery = debt recovery: How infrastructure financing models lead to overcapacity, debt, and disconnection
Bibliographic record
Abstract
Abstract Since the 1970s, the international community has pushed a commercial model for water supply based on utility autonomy and full‐cost recovery. This was supposed to deftly solve the persistent problems of poor service coverage and quality, insufficient revenue, and indebtedness. These problems were attributed to poor governance, considered inherent to government management and almost universal to utilities in low‐income cities, especially in the global South. A good dose of business‐like discipline would get these utilities on track. Things are never so simple. Instead, the international debt‐financing system was at the root of many problems that commercialization was supposed to solve, driving both the acquisition of new debt and a focus on large infrastructure projects that further increased debt burdens while failing to meet the needs of the urban poor. The real goal of commercialization was debt collection: to ensure that international lenders and international investors under financialization–are paid. This has led to unaffordable tariffs and consumer debt for utility services. Escaping this “debt trap” requires a new philosophy of infrastructure financing, one that democratizes decision‐making, focuses on smaller projects of social and environmental value, and considers “use value” rather than simple exchange‐value in assessments of what it means for an investment to be productive. This article is categorized under: Human Water > Human Water
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".