Social Concerns, Risk and The Acceptability of Forest Vegetation Management Alternatives: Insights for Managers
Bibliographic record
Abstract
Although public opinion and social issues have significant influence on policy-making, research on forest vegetation management (FVM) in Canada has a strong focus on biological aspects, with less attention being paid to social concerns. This paper reviews the social context in which FVM occurs. Individual views about FVM reflect a combination of values, beliefs, and attitude while also including differing perceptions of risks. Public views and the broader social acceptability of management decisions can be linked to five key factors: context, risk, aesthetics, trust, and knowledge. Judgements about acceptability will usually change over time and across specific situations and various segments of a population could make opposing judgements. We identify a variety of public concerns related to FVM in Canada, synthesizing research that can help resource managers consider the social impacts of their choices. We also note particular concerns related to Aboriginal peoples and the FVM workforce. Information about the benefits and disadvantages of FVM options can help resolve public concerns, but using technical information to convince the public is rarely successful. Forest management agencies and resource managers need access to reliable information about social values and concerns to make management decisions that will be socially acceptable.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".