When land, water and green‐grabbing cumulate: Hydropower expansion, livelihood resource reallocation and legitimisation in southwest China
Bibliographic record
Abstract
Hundreds of hydropower dam projects, of all sizes, have been initiated in Yunnan Province, China, since the late 1990s. This paper frames hydropower‐driven resource reallocations as resource grabs that combine aspects of land, water and green‐grabbing, investigating how two dams built along the Red River have impacted local communities and how corporate and governmental stakeholders have viewed local livelihood changes and considered compensation mechanisms. This research documents how hydropower expansion triggers changes in both land and water availability, in turn depriving riverside communities of a wide range of intersecting livelihood benefits. Villagers were compensated for some losses, but in ways that failed to address how impacts accumulated over time and how hydrologic changes would impact overall livelihood activities. Financial compensation and specific environmental and modernisation agendas legitimised resource reallocations together with the provincial, national and global development campaigns driving them. Considering how different actors experience, frame and address the impacts of hydropower development through a resource‐grabbing lens elucidates the compartmentalised approaches of distant hydropower actors as well as scholars. This study answers recent calls to mobilise the scholarship on resource‐grabbing in the service of shedding light on the socio‐political projects driving resource reallocations and their livelihood impacts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".