Futures for invasive alien species management: using bottom-up innovations to envision positive systemic change
Bibliographic record
Abstract
Abstract Invasive alien species (IAS) pose a key threat to biodiversity, the economy and human well-being, and continue to increase in abundance and impact worldwide. Legislation and policy currently dominate the global agenda for IAS, although translation to localised success may be limited. This calls for a wider range of responses to transform IAS management. An under-appreciated strategy to achieve success may come from bottom-up, experimental innovations (so-called “seeds”), which offer alternative visions of what may be possible for IAS management in the future. We present an application of a participatory process that builds on such innovations to create alternative visions of the future, with actionable pathways to guide change. Through a series of workshops with practitioners and academics, we used this process to explore alternative positive futures for IAS management in South Africa. We then identified a set of domains of change, that could enable these visions to be actioned by appropriate stakeholders. The domains of change highlight the social–ecological nature of the IAS sector, with interconnected actions needed in financial, cultural, social, technological and governance spheres. Key domains identified were the need to shift mindsets and values of society regarding IAS, as well as the need for appropriate and functional financing. This participatory futuring process offers a way to interrogate and scale bottom-up innovations, thereby creating optimism and allowing stakeholders to engage constructively with the future. This represents an important step in fostering the potential of bottom-up innovations to transform IAS management.
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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 | 0.038 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.035 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".