Science communication in experimental biology: experiences and recommendations
Bibliographic record
Abstract
During the century of Journal of Experimental Biology's existence, science communication has established itself as an interdisciplinary field of theory and practice. Guided by my experiences as a scientist and science writer, I argue that science communication skills are distinct from scientific communication skills and that engaging in science communication is particularly beneficial to early-career researchers; although taking on these dual roles is not without its difficulties, as I discuss in this Perspective. In the hope of encouraging more scientists to become science communicators, I provide: (i) general considerations for scientists looking to engage in science communication (knowing their audience, storytelling, avoiding jargon) and (ii) specific recommendations for crafting effective contributions on social media (content, packaging, engagement), an emerging, accessible and potentially impactful mode of science communication. Effective science communication can boost the work of experimental biologists: it can impact public opinion by incisively describing the consequences of the climate crisis and can raise social acceptance of fundamental research and experiments on animals.
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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 | 0.048 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".