Considerations for effective science communication
Bibliographic record
Abstract
It is increasingly common for scientists to engage in sharing science-related knowledge with diverse knowledge users—an activity called science communication. Given that many scientists now seek information on how to communicate effectively, we have generated a list of 16 important considerations for those interested in science communication: (1) Define what science communication means to you and your research; (2) Know—and listen to—your target audience; (3) Consider a diverse but coordinated communication portfolio; (4) Draft skilled players and build a network; (5) Create and seize opportunities; (6) Be creative when you communicate; (7) Focus on the science in science communication; (8) Be an honest broker; (9) Understand the science of science communication; (10) Think like an entrepreneur; (11) Don’t let your colleagues stop you; (12) Integrate science communication into your research program; (13) Recognize how science communication enhances your science; (14) Request science communication funds from grants; (15) Strive for bidirectional communication; and (16) Evaluate, reflect, and be prepared to adapt. It is our ambition that the ideas shared here will encourage readers to engage in science communication and increase the effectiveness of those already active in science communication, stimulating them to share their experiences with others.
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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 | 0.086 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.021 |
| Scholarly communication | 0.023 | 0.027 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.021 | 0.017 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".