Considerations for effective science communication
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it