Bibliographic record
Abstract
Around the world, the construction of large-scale dams has become a controversial environmental issue. A significant example of such a dam is the Three Gorges Dam on the Yangtze River in China. The Yangtze River, considered the cradle of Chinese civilization, has fundamentally shaped Chinese livelihood, culture, transportation, and agriculture. Despite international and local dissent, as of 2012, the Three Gorges Dam has become a reality. Having fundamentally altered the river system, the biodiversity of the area is now threatened by flooded habitats upstream, drought downstream and a change in nutrient distribution. However, to better understand the reasoning behind the construction of the Three Gorges Dam, a deeper historical, cultural and political basis needs to be recognized. This paper will investigate how the specific history of China has led to the construction of the Dam. Throughout the 20th century, political leaders envisioned the Dam as a symbol of Chinese industrialization and modernization. Ironically, it was considered the ultimate achievement in China’s development. Communist views of mastery over nature played an important role in such political views. These views are not limited to China; the idea that humans should control nature for economic benefit is still evident throughout the world. Beyond the context of China, the Three Gorges Dam is a symbol of the larger systems that value economic benefit and industrialization over a sustainable environment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".