Confronting the wicked problem of managing biological invasions
Bibliographic record
Abstract
The Anthropocene Epoch is characterized by novel and increasingly complex dependencies between the environment and human civilization, with many challenges of biodiversity management emerging as wicked problems. Problems arising from the management of biological invasions can be either tame (with simple or obvious solutions) or wicked, where difficulty in appropriately defining the problem can make complete solutions impossible to find. We review four case studies that reflect the main goals in the management of biological invasions – prevention, eradication, and impact reduction – assessing the drivers and extent of wickedness in each. We find that a disconnect between the perception and reality of how wicked a problem is can profoundly influence the likelihood of successful management. For example, managing species introductions can be wicked, but shifting from species-focused to vector-focused risk management can greatly reduce the complexity, making it a tame problem. The scope and scale of the overall management goal will also dictate the wickedness of the problem and the achievability of management solutions (cf. eradication and ecosystem restoration). Finally, managing species that have both positive and negative impacts requires engagement with all stakeholders and scenario-based planning. Effective management of invasions requires either recognizing unavoidable wickedness, or circumventing it by seeking alternative management perspectives.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".