Science communication is needed to inform risk perception and action of stakeholders
Bibliographic record
Abstract
Stakeholders are critical environmental managers in human-dominated landscapes. In some contexts, stakeholders can be forced to personally act following their own observations and risk perception instead of science recommendation. In particular, biological invasions need rapid control actions to reduce potential socio-ecological impacts, while science-based risk assessments are rather complex and time-delayed. Although they can lead to important detrimental effects on biodiversity, potential time-delayed disconnections between stakeholders' action and science recommendations are rarely studied. Using the case study of western European beekeepers controlling the invasive Asian hornet Vespa velutina nigrithorax for its suspected impact on honey bee colonies , we analysed mechanisms underlying personal actions of stakeholders and how they evolved in science disconnection. Personal actions of stakeholders were causal-effect linked with their risk observation but disconnected to time-delayed science predictions and recommendations. Unfortunately, these science-disconnected actions also led to dramatic impacts on numerous species of the local entomofauna. These results highlight the need to improve mutual risk communication between science and action in the early-stages of management plans to improve the sustainably of stakeholders’ practices. • Current global changes can lead to time lags between science and action. • We analyse stakeholder actions and how they evolved in science disconnection. • Stakeholder actions are disconnected to time-delayed science recommendations. • Science-disconnected actions impact local biodiversity. • Improving risk communication to biological invasion is urgently required.
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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 | 0.080 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.016 | 0.028 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".