A Shifting Tide: Recommendations for Incorporating Science Communication into Graduate Training
Bibliographic record
Abstract
Abstract Scientists who are skilled in communication reap professional and personal rewards. Unfortunately, gaps exist in fostering curricular and extracurricular training in science communication. We focus our article on opportunities for university‐ and department‐level leadership to train new scientists to communicate effectively. Our motivation is threefold: (1) communication training is key to being competitive in the increasingly diverse job market, (2) training early career scientists in communication “jump‐starts” personal and societal benefits, and (3) the authors represent a group of early career aquatic scientists with unique insights on the state of and need for training. We surveyed early career aquatic scientists about their science communication training experiences. In summary, survey respondents indicated that (1) science communication training is important; (2) graduate students are interested in training that is not currently available to them; (3) departments and advisors are moderately supportive of students participating in science communication, but less enthusiastic about providing training support; and (4) graduate students lack opportunities to put science communication training into practice. We recommend departments and institutions recognize the benefits of science communication training, develop a strategy to support such training, and facilitate individualized approaches to science communication.
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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 | 0.104 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.021 | 0.027 |
| Open science | 0.010 | 0.021 |
| Research integrity | 0.025 | 0.021 |
| Insufficient payload (model declined to judge) | 0.035 | 0.012 |
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".