Public perception of forest assisted migration (FAM): a useful approach which requires cautious implementation?
Bibliographic record
Abstract
Forest Assisted Migration (FAM) emerges as a promising strategy of adaptation to climate change (CC) in the forestry sector. This method integrates various sources of knowledge to identify optimal locations for future tree species establishment through human intervention. As climate change presents numerous challenges to Quebec’s forests, including shifts in suitable habitats for tree species, the FAM recommends a proactive approach to adapt to these changes. Recognizing the importance of addressing risks and following international resolutions, such as the Paris Agreement on Climate Change (COP21) and Convention on Biological Diversity (CBD), social perception becomes a critical consideration in the decision-making process regarding FAM. To investigate this issue, we conducted semi-structured interviews with 18 key stakeholders in Quebec, including officials from the Ministry of Natural Resources and Forestry, employees of private forestry technical companies, forestry researchers, private forest owners, non-governmental organizations (NGO) members, and other forest stakeholders. We analyzed the data collected in this phase through thematic discrimination, focusing on (i) acceptability (ii) risk perception (iii) feasibility, and (iv) purpose. Then, we compared the discourses with data from other publications addressing the social aspects of FAM. To develop this reflection, we found it important to use a conceptual framework that encompasses the articulation among some concepts of intervention through FAM vs. non-intervention, public trust in government authorities, risk perception and scientific knowledge. The interviews revealed a general inclination among stakeholders to support FAM, dependent upon cautious implementation with pilot projects and studies serving as references for future large-scale applications. Concerns raised by stakeholders included seed production, adequate experimental monitoring, and careful species selection. While some stakeholders were more knowledgeable about current FAM research, others emphasized the importance of economic viability, public participation in decision-making, and transparency in forestry processes. We identified communication and public involvement as fundamental aspects for advancing the perspective of FAM implementation in Quebec; it is crucial to recognize the presence of humans in FAM target areas and their potential contributions to implementation. Moving forward, forest authorities responsible for FAM should prioritize stakeholder engagement at all levels to ensure a socially inclusive strategy of adaptation that respects a wide range of considerations.
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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 | 0.071 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.035 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".