Communicating research with the public: evaluation of an invasive earthworm education program
Bibliographic record
Abstract
Ecologists are increasingly encouraged by funding agencies and professional societies to communicate their research with the public. However, most receive relatively little training in how to do this effectively. Furthermore, evaluation of whether such an investment by ecologists actually achieves conservation objectives is rare. We created an education program, involving print, television, radio, and internet media, to increase awareness about earthworm invasions and to discourage anglers from dumping earthworm bait. Using pre- and post-surveys, we evaluated our program’s success in reaching its target audience and in changing knowledge and behavior. Few participants (4.1%) recalled seeing the program material and knowledge of the fact that earthworms are non-native in Alberta remained low (15.8% before, 15.1% after). Further, after being told about the negative effects of earthworms in forests, 46.7% of the anglers surveyed stated they would not change their bait disposal behavior in the future, with many commenting that they did not believe earthworms could be harmful. These results highlight the importance of evaluating education programs, rather than assuming they are successful. Given many participants’ doubts that earthworms have negative effects, both regulations and education may be needed to reduce earthworm introductions.
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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 | 0.031 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".