Preparing for and Responding to Disturbance: Examples from the Forest Sector in Sweden and Canada
Bibliographic record
Abstract
Coping or adaptation following large-scale disturbance may depend on the political system and its preparedness and policy development in relation to risks. Adaptive or foresight planning is necessary in order to account and plan for potential risks that may increase or take place concurrently with climate change. Forests constitute relevant examples of large-scale renewable resource systems that have been directly affected by recent environmental and social changes, and where different levels of management may influence each other. This article views disturbances in the forest sectors of Sweden and Canada, two large forest nations with comparable forestry experiences, in order to elucidate the preparedness and existing responses to multiple potential stresses. The article concludes that the two countries are exposed to stresses that indicate the importance of the governing and institutional system particularly with regard to multi-level systems including federal and EU levels. While economic change largely results in privatization of risk onto individual companies and their economic resources (in Canada coupled with a contestation of institutional systems and equity in these), storm and pest outbreaks in particular challenge institutional capacities at administrative levels, within the context provided by governance and tenure systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".