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Science communication scholars use more and more segmentation analyses: Can we take them to the next level?

Tobias Füchslin
Published in: Public understanding of science (Bristol, England) (2019)
Science communication scholars are publishing more and more segmentation analyses as they further our understanding of different audiences and their characteristics. They follow different aims, are therefore difficult to compare and do not lend themselves to more generalisable and theoretical knowledge production. Our field has the potential to follow a demand for more systematic efforts by taking advantage of our high-quality representative data sets focusing on public perceptions of science. Beforehand, however, science communication scholars using segmentation analyses have to identify common goals and overcome a number of hurdles concerning variable selection, methodological approaches, and transparency. Ultimately, a collaborative and systematic application of segmentation analyses could result in truly relevant insights for our field.
Keyphrases
  • deep learning
  • convolutional neural network
  • public health
  • healthcare
  • quality improvement
  • mental health
  • artificial intelligence
  • machine learning
  • electronic health record
  • global health
  • risk assessment
  • human health