Lost in Development's Shadow: The Downstream Human Consequences of Dams
Bibliographic record
Abstract
The World Commission on Dams (WCD) report documented a number of social and environmental problems observed in dam development projects. The WCD gave particular emphasis to the challenges of properly resettling populations physically displaced by dams, and estimated the total number of people directly displaced at 40-80 million. Less attention has been given, however, to populations living downstream of dams whose livelihoods have been affected by dam-induced alterations of river flows. By substantially changing natural flow patterns and blocking movements of fish and other animals, large dams can severely disrupt natural riverine production systems – especially fisheries, flood-recession agriculture and dry-season grazing. We offer here the first global estimate of the number of river-dependent people potentially affected by dam-induced changes in river flows and other ecosystem conditions. Our conservative estimate of 472 million river-dependent people living downstream of large dams along impacted river reaches lends urgency to the need for more comprehensive assessments of dam costs and benefits, as well as to the social inequities between dam beneficiaries and those potentially disadvantaged by dam projects. We conclude with three key steps in dam development processes that could substantially alleviate the damaging downstream impacts of dams.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".