Coordinating invasive plant management among conservation and rural stakeholders
Bibliographic record
Abstract
(Uploaded by Plazi for the IPBES Invasive Alien Species Assessment) Collective action among conservation and rural land managers is required to protect natural and rural ecosystems from the spread of invasive plants. Achieving such tenure-blind collective action is a considerable policy challenge and social research on this topic is in its infancy, is rural-focused and rarely addresses multiple species concurrently. This study explores the nature and extent of collective action among conservation and rural stakeholders managing multiple invasive plant species in south-west Alberta, Canada. Thirty telephone interviews were conducted with staff of national and provincial parks, non-government organisations and government agencies, as well as ranchers and consultants operating within the Oldman Watershed. The results showed three key types of collective action—participatory, linked and collaborative—occurring across the landscape. Collaborative invasive plant management (IPM) was the most likely to bring rural and conservation land managers together to address multiple species but was highly resource intensive and confined to public lands. A polycentric system of governance may enable landscape-wide IPM to be achieved if it can link existing collaborative efforts as well as establish and maintain new relationships among rural and conservation stakeholders. Organisations that encompass multiple land uses, such as watershed councils and municipal districts, may be best placed to bring diverse stakeholders together to develop a shared plan, facilitate social learning and demonstrate on-ground action at multiple scales across land uses.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".