Dam removal blind spots: debating the importance of community engagement in dam decommissioning projects
Bibliographic record
Abstract
This article calls for social justice within the transition from dam building to decommissioning. Dam decommissioning is escalating in the global north, and sooner than later, the tied will spread to the global south. Though dam removal is an essential strategy for riverine landscape restoration, it may yield negative social outcomes for communities living along dams. Ecological restoration must not be achieved at the expense of local communities. Decisions on dam removal are predominantly made by experts and government agencies, often to the exclusion of local communities. For this reason, the decisions to remove several dams in the global north have been opposed by local communities leading to suspension or, in worst-case scenarios, reversal of such decisions. By referring to cases from Europe, USA, and Canada where dam removals have been opposed, this article argues for better incorporation of local communities in decision-making. Community consultations and consent are key in achieving successful decommissioning with minimal harm on communities. Yet, they have not received sufficient attention in dam removal conversations. The socio-economic issues are also not sufficiently interrogated in the literature on dam removal. We underscore this gap and provides recommendations for best social performance in dam removals.
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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 | 0.060 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.030 | 0.026 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.008 | 0.011 |
| 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".