Opportunities for better use of collective action theory in research and governance for invasive species management
Bibliographic record
Abstract
Controlling invasive species presents a public-good dilemma. Although environmental, social, and economic benefits of control accrue to society, costs are borne by only a few individuals and organizations. For decades, policy makers have used incentives and sanctions to encourage or coerce individual actors to contribute to the public good, with limited success. Diverse, subnational efforts to collectively manage invasive plants, insects, and animals provide effective alternatives to traditional command-and-control approaches. Despite this work, there has been little systematic evaluation of collective efforts to determine whether there are consistent principles underpinning success. We reviewed 32 studies to identify the extent to which collective-action theories from related agricultural and environmental fields explain collaborative invasive species management approaches; describe and differentiate emergent invasive species collective-action efforts; and provide guidance on how to enable more collaborative approaches to invasive species management. We identified 4 types of collective action aimed at invasive species-externally led, community led, comanaged, and organizational coalitions-that provide blueprints for future invasive species management. Existing collective-action theories could explain the importance attributed to developing shared knowledge of the social-ecological system and the need for social capital. Yet, collection action on invasive species requires different types of monitoring, sanctions, and boundary definitions. We argue that future government policies can benefit from establishing flexible boundaries that encourage social learning and enable colocated individuals and organizations to identify common goals, pool resources, and coordinate efforts.
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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 | 0.053 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.003 | 0.030 |
| Scholarly communication | 0.012 | 0.030 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".