Communicating with the public: opportunities and rewards for individual ecologists
Bibliographic record
Abstract
Many ecologists are interested in communicating science to the public and addressing societal concerns about environmental issues. Individual ecologists need to consider whether, when, and how this should be done. We propose that public outreach activities can be beneficial for ecologists at all stages of their career. There are diverse opportunities for such involvement, and these can vary enormously in terms of time and expertise required. Trends within the science of ecology, especially research focused on social–ecological systems, are likely to promote increased interactions with stakeholders and policy makers. To be effective in these interactions, ecologists should consider new approaches to communication and be aware of the potential roles scientists can play in public policy debates. Professional ecologists need to engage with non‐scientific audiences; a review of such activities should be included in considerations for promotion, recognition, and awards, while also acknowledging variations in the inclinations and abilities of individual scientists. There are, however, few current standards for how much time ecologists should commit to public outreach, how time allocation might change over a career, or how to evaluate the quality of such activities. We ask ecologists to consider ways to evaluate the quality of interactions with the public and how to reward these efforts appropriately.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".