A potential role for EIA in Finnish forest planning: learning from experiences in Ontario, Canada
Bibliographic record
Abstract
Reconciling diverse forest values within policy and decision-making processes is an ongoing challenge in forestry. The use of environmental impact assessment (EIA) provides potential for improving forest management and making it more responsive to diverse interests. This paper examines EIA in Canadian and Finnish forest planning. In Finland there has been a reluctance to see EIA as a tool for forest planning while in Canada some provinces have long applied EIA to forest management. Ontario, Canada, provides one example of applying EIA to forest planning at a range of scales in order to advance integrated planning and help conflict management. The paper provides a brief analysis of the Finnish forest planning system, an illustration of the Ontario EIA forest management experience, and then considers the application of EIA to Finnish forest management. The paper concludes that EIA may be workable for Finnish state forests and would likely enhance planning and management, but given the existing institutional frameworks EIA would be difficult to apply to private forests.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.043 | 0.012 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".