Bibliographic record
Abstract
Abstract Introductions of nonnative invasive species can harm ecosystems, heighten the risk of native species extinctions and population reductions, and lead to substantial economic damages on a worldwide scale. Increasingly, economists have made contributions that help other researchers, policymakers, and society better understand the economic implications of invasive species as well as the most economically efficient approaches for managing them. The complexity of invasive species management problems has pushed economists to ask novel economic questions and to develop new analytical approaches in order to address specific policy questions. There are three areas, in particular, where the economic analysis of invasive species management has led to significant innovations. First, there are substantial challenges to quantifying economic damages from invasive species for application in benefit−cost analysis. The challenges relate to defining the counterfactual state of an invaded ecosystem with and without management/policy and to the fact that, in a given ecosystem, estimates of economic damages are available for only a subset of the species and for only a subset of damages for any one species. Recent economic research has proposed innovative approaches to systematically dealing with these two issues in the context of invasive species that have implications for applied benefit−cost analysis more broadly. Second, unique among natural resource management problems, invasive species have the feature that their current and future extents are directly tied to a country’s participation in international trade. This feature has led to innovative research into the design of efficient measures to prevent or delay invasive species introductions along national borders, and into the trade-offs between these measures and the use of border controls as protectionist tools. The issues of optimal inspection policy and the use of nontariff barriers as a form of covert protectionism both have implications beyond invasive species management. Third, researchers have developed bioeconomic models that integrate economic and biological factors in order to analyze strategies to more cost-effectively reduce the damages caused by invasive species. These modeling efforts have dealt with issues related to temporal and spatial dynamics of the biological invasions, imperfect information regarding the extent of the invasion and the effectiveness of management, linkages between management applied at different stages of an invasion, and complications arising from ecosystems’ crossing over ecological thresholds due to invasions. In the face of increasingly rapid ecosystem change due to global climate change, increases in extreme weather, urban encroachment into wild lands, and other factors, many of these features of invasive species management problems are likely to become features of ecosystem management more broadly in the near future if they are not so already.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".