Social and Environmental Justice for Communities of the Mekong River
Bibliographic record
Abstract
During the mid-twentieth century the construction of hydroelectric dams in developing countries became a contentious issue in economic and political arenas. Governments and pro-damming parties, particularly those with a direct commercial interest, often consider the dams to be necessary for economic growth, as well as being in the national interest. Others directly impacted by the altered hydrology and ecology, as well as experts concerned with environmental and demographic impacts, are less sure about the benefits of hydro-electric dams. Concerns about the environmental and human impacts of dams are heightened on a waterway such as the Mekong River, which flows through six sovereign states. When the impacts are to be felt across entire regions, ensuring just outcomes for all the stakeholders is crucial to long-term regional political stability and economic wellbeing. Engineers are pivotal in the design and construction of dams, and engineering teams also play an increasingly important role in assisting communities impacted by the altered hydrology. This article focuses on the community and environmental implications of engineering decisions on the Mekong River, and suggests ways in which engineers involved in dam design and construction can play a role in ensuring that socially and environmentally just outcomes are achieved.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.033 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".