On the effectiveness of public awareness campaigns for the management of invasive species
Bibliographic record
Abstract
Summary Invasive species can have disastrous effects on the ecosystems they invade, requiring costly, labour-intensive mitigation. Public awareness campaigns are often used as a tool to reduce these species’ impacts. While heralded as useful and cost-effective, little evidence suggests that these campaigns contribute to meaningful biological outcomes. Furthermore, awareness campaigns are relatively understudied despite their usage as a common approach to mitigating invasive species. We conducted a literature review to assess publications that evaluated the efficacy of public awareness campaigns for managing invasive species. Out of 4382 papers initially extracted for analysis, we determined that 24 of them included studies conducted on awareness campaigns for invasive species. Four public awareness campaigns were deemed a ‘success’, and the other campaigns’ success was indeterminable due to study design. Our study revealed that inconsistencies in defined end points, unclear procedures and variability of campaigns contribute to there being insufficient evidence to determine the efficacy of public awareness campaigns. To evaluate the true efficacy of public awareness campaigns, we recommend that organizations conducting such campaigns implement rigorous and standardized assessments (e.g., Before–After Control–Impact designs or Bayesian analyses) that include measures of not just changes in the knowledge and behaviour of target audiences, but also relevant biological 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 | 0.071 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".