Perceptions of climate change risk to forest ecosystems and forest-based communities
Bibliographic record
Abstract
Perception of risk or subjective risk is playing an increasingly important role in risk assessment. This paper describes a study that investigated perceptions of climate change risk to forest ecosystems and forest-based communities among a sample of Canadian forestry experts. Data were collected by questionnaire from participants at a climate change and forestry workshop, sponsored by the Canadian Climate Impacts and Adaptation Research Network Forest Sector and the McGregor Model Forest held in Prince George, British Columbia in February 2003. These forestry experts were somewhat concerned about the impacts of climate change, and they appeared unlikely to oppose strategies for preparing for and adapting to climate change. The respondents felt that the effects of climate change on forests and forest-based communities are not well understood by the general public or forest managers. They also felt that there is a relatively high level of uncertainty about the effects of climate change, especially with respect to forest-based communities. These results have important implications, including reinforcement of the need for greater awareness of climate change risks and for increased research and monitoring effort targeted at reducing levels of uncertainty about future impacts at local scales. Key words: climate change, risk perceptions, forest ecosystems, forest-based communities
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".