From anti-science to environmental nihilism: the Fata Morgana of invasive species denialism
Bibliographic record
Abstract
Invasive species denialism (ISD) is a controversial and hitherto underexplored topic, particularly with regard to its potential impacts on stakeholder engagement in support of invasive species management and policy. We examined how ISD is framed within the Great Lakes invasive species community, as well as the impacts of excluding and including those perceived as denialists in engagement efforts. We interviewed key informants in the region to gain an understanding of their framings of ISD, as well as focus groups allowing participants to discuss the impacts of exclusion and inclusion of stakeholders during the engagement process. ISD discussions were organised into three framings: 1) invasive species denialism; 2) invasive species cynicism; 3) invasive species nihilism. Participants raised concerns about outright exclusion of stakeholders and offered recommendations for mitigation of the impacts of inclusion of proponents of ISD in during stakeholder engagement. Our results have shown that a better understanding of the different framings of ISD is crucial to improve communication with stakeholders and to better inform responses and mitigation efforts. The newly defined framings of invasive species cynicism and invasive species nihilism demonstrate that more targeted responses to specific forms of ISD are needed to improve stakeholder engagement outcomes.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.041 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".