A Human Security Framework for the Management of Invasive Nonindigenous Plants
Bibliographic record
Abstract
Abstract Few individuals or governments have suggested that invasions by nonindigenous species are relevant to the broader issue of human security, despite a growing awareness of the ecological, economic, and societal impacts associated with invasive nonindigenous species (INIS). We propose that by framing management actions in a human and environmental security context, the threats (and benefits) posed by INIS to individuals and communities can be explicitly articulated and debated. This framework allows multiple stakeholders to bring their concerns to bear upon specific policy, and attempts to integrate broad environmental concerns within its parameters. We use the case of ecosystem-based management of invasive nonindigenous plants as an example of the utility of a human security framework. The dominant management approach to these species remains focused on the individual species, despite increasing calls for the implementation of ecosystem-based management strategies. Ecosystem-based management is supported by generalized and widely accepted mechanisms of plant community dynamics, such as succession, disturbance, and interspecific competition, but these scientific arguments do not consistently carry weight at the policy level and with the broader public. A human security framework may provide an approach for overcoming this resistance by placing the debate over management within the social and political context of the wider community. Overall, human security can allow applied ecologists to be better positioned to meet the challenges of communicating the need for science-based 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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".