Trickle‐down debt: Infrastructure, development, and financialisation, Medellín 1960–2013
Bibliographic record
Abstract
In many Latin American cities, infrastructure was largely financed through development lending over the second half of the 20th century. Exacerbated by debt crises and currency devaluations, public utilities became holders of significant levels of negative value. This encouraged public debt financialisation in order to mitigate the effects of shifting interest rates and devaluation. For David Harvey, negative value is the hallmark of contemporary capitalism whereby one must produce, not for profit, but to retire debt. This statement can be applied to indebted utilities, in the sense that the focus of utility governance – and its relationship towards those dependent on it for services – becomes reoriented towards debt management – or governing by debt. Full‐cost recovery emerges in this context as a mechanism to pay down the infrastructure debt held by utilities, which quickly led to increasing levels of user indebtedness. Service disconnection and pre‐paid metering emerge as processes to recover this user debt by enforcing a culture of payment through service exclusion. In these ways, the responsibility for infrastructure debt ‘trickles down’ in small – but individually significant – amounts to persons and households, enrolling them in the logic of debt (re)payment. This paper examines these issues through a case study of urban infrastructure financing, debt, and tariffs in Medellín, Colombia from 1960 to 2013.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".