Re-Theorizing Politics in Water Governance
Bibliographic record
Abstract
This Special Issue on water governance features a series of articles that highlight recent and emerging concepts, approaches, and case studies to re-center and re-theorize “the political” in relation to decision-making, use, and management—collectively, the governance of water. Key themes that emerged from the contributions include the politics of water infrastructure and insecurity; participatory politics and multi-scalar governance dynamics; politics related to emergent technologies of water (bottled or packaged water, and water desalination); and Indigenous water governance. Further reflected is a focus on diverse ontologies, epistemologies, meanings and values of water, related contestations concerning its use, and water’s importance for livelihoods, identity, and place-making. Taken together, the articles in this Special Issue challenge the ways that water governance remains too often depoliticized and evacuated of political content or meaning. By re-centering the political, and by developing analytics that enable and support this endeavor, the contributions throughout highlight the varied, contested, and important ways that water governance needs to be recalibrated and enlivened with keen attention to politics—broadly understood.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".