Legal Tools of Public Participation in the Environmental Impact Assessment Process and Their Application in the Countries of the Barents Euro-Arctic Region
Bibliographic record
Abstract
The article focuses on research of existing legal tools of public participation in the Environmental Impact Assessment (EIA) process and on practical issues of their application in the countries of the Barents Euro-Arctic Region (BEAR). The EIA is mandatory for projects which can have negative impacts on the environment and/or human health. Public participation in the EIA is one of the instruments used both on the international and national level that helps prevent or minimise the negative consequences of the project for the environment and human health. This article is based on research of national EIA legislation and on the analysis of the findings from interviews conducted with private and public organisations during benchmarking visits and fact-finding trips to the northern regions of Finland, Norway, Sweden and Northwest Russia. In addition, feedback was collected from participants during four seminars. Participatory methods, focused on public‒private communication and participation during the environmental impact assessment process, provides the theoretical basis for the article. This research results from work in a two-year strategic project funded by the Finnish Funding Agency for Innovation Tekes.
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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 | 0.106 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".