Rethinking water corporatisation: A 'negotiation space' for public and private interests, Colombia (1910-2000)
Bibliographic record
Abstract
As part of neoliberal reforms to public service delivery, the corporatisation of water supply has been of increasing concern since the late 1990s. Typically, both promoters and detractors frame it within neoliberal theory: it is the next best (or worst) thing to privatisation, enabling the ostensibly independent, commercial and technical management of utilities. In Colombia, however, city-owned water supply corporations are far from new. They were adopted across the country’s main cities at the beginning of the 20th century. Colombia’s century-long experience with corporatised water supply is instructive. The case reveals a model that emerged in the context of challenges common to Southern cities, rather than as a 'solution' imposed from the North, the deep inter-linkages between public and private sectors in the evolution of publicly owned corporations and thus the limited nature of utility autonomy under corporatisation. In sum, corporatisation – imagined as a technology for the 'government of government' – cannot escape the shifting social realities in which it is immersed. It therefore emerges as a technology not for the excising of government authority but for the negotiation of public and private interests in (and influence over) utility services in contexts of relatively limited government autonomy from the private sector.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".