Ten simple rules for improving communication among scientists
Bibliographic record
Abstract
Communication is a fundamental part of scientific development and methodology. With the advancement of the internet and social networks, communication has become rapid and sometimes overwhelming, especially in science. It is important to provide scientists with useful, effective, and dynamic tools to establish and build a fluid communication framework that allows for scientific advancement. Therefore, in this article, we present advice and recommendations that can help promote and improve science communication while respecting an adequate balance in the degree of commitment toward collaborative work. We have developed 10 rules shown in increasing order of commitment that are grouped into 3 key categories: (1) speak (based on active participation); (2) join (based on joining scientific groups); and (3) assess (based on the analysis and retrospective consideration of the weaknesses and strengths). We include examples and resources that provide actionable strategies for involvement and engagement with science communication, from basic steps to more advanced, introspective, and long-term commitments. Overall, we aim to help spread science from within and encourage and engage scientists to become involved in science communication effectively and dynamically.
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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 | 0.087 | 0.189 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".