Unpacking water governance dynamics and its implications for household water security in post-disaster resettlement communities in the Philippines
Bibliographic record
Abstract
Water security is important globally for sustaining households, communities, and the environment. However, most studies have focused on water availability and accessibility, whereas research examining the role of power and politics in shaping water insecurity remains marginal. This study contributes to this research gap by unpacking the overlapping drivers and politics in water governance dynamics that co-produce the water insecurity of Typhoon Haiyan disaster-displaced households in resettlement villages in the Philippines. Using political ecology and water governance perspectives, we ask, What does household water insecurity look like in post-disaster resettlement villages in the Philippines? What are its drivers and how do politics and governance dynamics impact the provision of water services to these villages? Our findings suggest five overlapping drivers: the haphazard relocation of internally displaced persons to areas without access to basic facilities like water; the institutional disharmony and late involvement of water institutions in the resettlement processes; the influence of governance regime in the rapid but substandard housing development, including water distribution systems; the micropolitics in water district management affecting water projects; and the impact of maladaptive resettlement outcomes on households’ capacity to afford water. Ultimately, this study demonstrates how various drivers, including power relations and contestations in water governance, lead to household water insecurity outcomes. It ends by providing brief policy recommendations to improve institutional arrangements for the better governance of water services to resettlement communities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".