Neither the public nor experts judge species primarily on their origins
Bibliographic record
Abstract
SUMMARY In contemporary environmental conservation, species are judged in terms of their origin (‘nativeness’), as well as their behaviour and impacts (‘invasiveness’). In many instances, however, the term ‘non-native’ has been used as a proxy for harmfulness, implying the need for control. Some scientists have attempted to discourage this practice, on the grounds that it is inappropriate and counterproductive to judge species on their origin alone. However, to date, no empirical data exist on the degree to which nativeness in itself (that is, a species’ origin) shapes people's attitudes towards management interventions in practice. This study addresses this void, demonstrating empirically that both the public and invasive species professionals largely ignore a species’ origin when evaluating the need for conservation action. Through a questionnaire-based survey of the general public and invasive species experts in both Scotland and Canada, the study revealed that perceived abundance and damage to nature and the economy, rather than non-nativeness, informed attitudes towards species management, empirically substantiating the claim that a species’ perceived abundance and impact, and not its origin, is what really matters to most people. Natural resource management should thus focus explicitly on impact-related criteria, rather than on a species’ origin.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".