Media visibility as a driver of scientific and social impact
Bibliographic record
Abstract
Well-known paradigms such as public understanding of science, public engagement in science and technology, and media visibility affect the perception of science in society but also the dynamics of the relationship between scientists, the public, and the media. The digital environment and social media have pushed the boundaries and created different aspects of visibility but have also raised issues such as the risk of data theft, misuse, manipulation or out-of-context use. Not only can media manipulate scientifically accurate information but can also spread misinformation.It is argued that science must be visible not only to scientists but also to the public in order to gain legitimacy, advance knowledge, promote positive attitudes, and increase engagement. This kind of visibility is at the forefront of the open science movement, which advocates transparency, openness, and reproducibility.Media and the digital environment have exponentially increased the availability of scientific knowledge to the general public and encouraged a growing number of scientists to tell their own stories on social networks or actively participate in public and media discussions, gaining in popularity along the way. The question arises, does this personal popularity contribute to the overall popularity of science and does it increase awareness of its significant impact on society and technology? Also, there is a continuous fear that scientific knowledge is vulnerable to misunderstanding or misinterpretation.This panel entitled Media visibility as a driver of scientific and social influence will discuss perspectives based on trust, transparency, and ethics in communication between scientists and journalists and take a look at activities that can increase the visibility of science in the media, challenges involved, and at the role of scientists and their reputation in communication with the general public. We will also discuss the limit(ation)s of strategic management of visibility, especially online, which can very quickly become uncontrolled, damage a reputation or two, and expose scientists to public criticism and even hostility.
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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 | 0.012 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".