HESS Opinions: Science in today's media landscape – challenges and lessons from hydrologists and journalists
Bibliographic record
Abstract
Abstract. Media such as television, newspapers and social media play a key role in the communication between scientists and the general public. Communicating your science via the media can be positive and rewarding by providing the inherent joy of sharing your knowledge with a broader audience, promoting science as a fundamental part of culture and society, impacting decision- and policy-makers, and giving you a greater recognition by institutions, colleagues and funders. However, the interaction between scientists and journalists is not always straightforward. For instance, scientists may not always be able to translate their work into a compelling story, and journalists may sometimes misinterpret scientific output. In this paper, we present insights from hydrologists and journalists discussing the advantages and benefits as well as the potential pitfalls and aftermath of science–media interaction. As we perceive interacting with the media as a rewarding and essential part of our work, we aim to encourage scientists to participate in the diverse and evolving media landscape. With this paper, we call on the scientific community to support scientists who actively contribute to a fruitful science–media relationship.
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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 | 0.024 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.028 | 0.019 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.013 | 0.009 |
| 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".