Exploring Social, Physical, and Mental Risks of Ecological Modernization Through Dam Construction: A Case Study of PT. Vale Indonesia's Mining Activities
Bibliographic record
Abstract
Mining activities can have a significant impact on ecology, either directly or indirectly.From 1977 to 2011, PT.Vale Indonesia had continuously carried out ecological modernization to produce a supply of energy for production.Even though it has carried out ecological modernization through the construction of dams, it still poses some bad risks.This study aims to analyze the impacts and risks arising from ecological modernization through the dam construction.Data collection included interviews, observations, focus group discussions, and documentation.This study also leveraged the Nvivo 12 Plus analysis tool for coding the data.The findings of this study indicate that ecological modernization through dam construction has significant impacts and risks.Physical impacts and risks include flood portraits that impact the activities of the surrounding community, including residents' settlements and plantations.The social impacts and risks include the loss of people's livelihoods, particularly in the agricultural and plantation sectors.The worst conditions of this social risk tend to erode social feelings, thus leading to the birth of a society without feelings, sensitivity, togetherness, and social responsibility in the social community.Mental risks include the community's mental health due to the accumulation of physical and social risks.Important implications of these findings for policymakers, practitioners, and other stakeholders are involved in the ecological modernization and construction of dams.These findings highlight the importance of considering ecological, physical, social, and mental impacts in the planning and implementing of projects such as dam construction.Stakeholders should focus on protecting the environment, physical security, and the social and mental well-being of the community.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".