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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".