Social impact assessment in the Russian Federation: does it meet the key values of democracy and civil society?
Bibliographic record
Abstract
Contemporary social impact assessment (SIA) is rooted in the concepts of civil society and democracy. We analyse whether SIA as practiced in the Russian Federation as part of environmental impact assessment (EIA) is consistent with the key values of civil society and democracy. We consider whether the Russian EIA requirements enable preparation of meaningful assessments that effectively contribute to the decision-making processes that affect people’s lives. We review the Rsussian EIA legislation and its requirements for SIA and social baseline, and consider the EIA/SIA practice undertaken in response to these requirements. We specifically analyse the Karmen coal mining project in South Yakutia. We compare the EIA documents completed according to national requirements against the Environmental and Social Impact Assessment (ESIA) documents prepared to be consistent with international standards, as defined by the International Finance Corporation Performance Standards. We conclude that the national requirements for SIA in Russia and the way they are implemented do not encourage the development of meaningful SIAs that comply with the key concepts and social values of SIA, civil society and democracy.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".