Chamera Hydro-Electric Power Project (Chep-1), Khairi: Looking beyond the Horizon of Hydroelectricity and Profit, Giving New Meaning to Life
Bibliographic record
Abstract
National Hydro-electric Power Corporation Limited (NHPC Ltd) was the biggest player in Indian hydro power industry. NHPC was in the business of hydro power generation. NHPC was spread all over India and generated about 3740 Mega Watt (MW) of electricity annually. Hydro-electric power projects brought associated challenges like loss of forest and village land because of the creation of the water reservoirs and dam infrastructure, etc., of the project. Further, this led to the displacement of people, which impacted their livelihood. NHPC had to effectively and efficiently minimize the social and environmental impacts created by its business initiatives in the projects. NHPC undertook Corporate Social Responsibility (CSR) initiatives to address these issues. The case writes about one such project (Chamera Hydro-electric Power Station -I, CHEP-1) of NHPC The case illustrates how NHPC bettered the lives of the people around CHEP-1. NHPC had major expansion and growth plans to cater to the growing energy demand of India. NHPC management aimed at contributing not only to its economic goals but also to the society where it worked. For that, NHPC had to make the CSR initiatives in its projects comparable to the CHEP-1 story
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".