LESSONS FROM SCENARIO PLANNING FOR WILDLIFE MANAGEMENT IN THE SOUTHWEST YUKON
Bibliographic record
Abstract
The southwest Yukon social-ecological system (SES) is marked by complex changes, including a climate induced directionally changing landscape, an increasing shift away from traditional subsistence lifestyles, and changing species composition. The addition of “new” ungulate species through human and non-human introductions has spawned many management questions. This study developed qualitative scenarios through a participatory process, utilizing scientific and traditional knowledge from within the social-ecological system’s local context. The study worked with local management groups to address two main objectives: 1.) Collaboratively envision alternate future scenarios with management groups from which to collaboratively develop management goals for wood bison, elk, and mule deer to cope with the changing social and ecological landscape of the southwest Yukon and 2.) Discover resource managers’ and local stakeholders’ perceptions of scenario planning as a method identify wildlife management goals. A series of three workshops with the Alsek Renewable Resource Council, the Yukon Wood Bison Technical Team, and the Yukon Elk Management Planning Team addressed the first objective, while two surveys addressed the second objective. Major findings included southwest Yukon-specific wildlife management goals and considerations for using scenario planning in a wildlife management context. The scenarios themselves warn of plausible events that might unfold, such as novel disease and pest outbreaks. Several participants mentioned that the value attributed to different species will change based on scenario context. This prompts warnings for wildlife managers not to “shut the door” on a species today that may be highly valuable for solving food security challenges of the future. Findings suggest that one of scenario planning’s most significant contribution is a forum for people to share perspectives and develop trust and understanding of one another. All participants valued the holistic and long-term thinking aspects of scenario planning, seeing it as a complementary tool to enhance existing planning processes. Major resource management plans and/or resource development projects in the future should consider using a scenarios approach to better articulated goals in terms of whole system impacts.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".