From austericide to recommoning: counter-imaginaries for democratizing water governance
Bibliographic record
Abstract
We address a key question around the extent to which a commons-oriented imaginary could offer alternatives to further democratize water governance by shifting public water governance institutions toward collective governance mechanisms. Two cities that have successfully remunicipalized their water governance and engaged with commons-inspired governance arrangements are compared: Terrassa in Spain and Naples in Italy. The cases are both considered deviant examples of successful water remunicipalization that pushed a commons logic to public governance. Results indicate that although the success of Naples finds its strength in changing legal frameworks to recognize and protect water as a common good, the success of Terrassa is in the daily recommoning practices of citizens through its newly established Citizen Water Observatory. A discussion is presented on the extent to which each approach has succeeded in democratizing water governance, according to the definition of democracy as a continuing effort toward collective management of affairs by a community. We point to both strengths and pitfalls of a commons-oriented governance approach while assessing the type and degree of transformation made to local public water governance institutions in each case. We caution that commoning is not a panacea but rather one approach in nested governance to resist market logics imposed on water resources.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.070 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".