Project Background

The project is concerned with how state-of-the-art scientific knowledge is translated, or, possibly, not translated or mistranslated, in texts accessed by young people aged 11-16. The research focusses on texts produced around climate change since this is a socio-scientific issue central to young people’s future lives as active citizens.

Eleven- to 16-year-olds find out about scientific issues from a variety of educational and popular texts, as well as from online sources and social media; they are unlikely to be able to read the texts in which scientists communicate their research findings, such as articles in specialised journals. However, the translation of information across genres may result in distortion; for instance, there is some evidence that public understanding of the human role in climate change is significantly at odds with the current scientific consensus.

To investigate translation in this context, we will conduct linguistic analyses of three large language datasets, composed of collections of texts about climate change,

representing the following:

  1. the language of science used by experts, represented by research articles and policy texts.
  2. the language of texts that young people access, represented by popular and educational materials, including curriculum materials, educational websites, popular science texts, internet forums, Twitter feeds and other texts used by young people.
  3. the language used by young people interviewed about climate change.

We will analyse a group of linguistic devices including metaphors, metonyms, words combinations (collocations) and the use of technical terms. We will compare the analyses of the three data-sets and identify commonalities and divergences in what is communicated and how. By analysing the interviews with young people, we will also consider how scientific information, attitudes and probabilities are understood and reframed by the young people themselves.

We will use corpus linguistic software to perform preliminary analyses on the data-sets. For example, we will identify which words and semantic fields are used most frequently in each, and to compare these data-sets. We will also perform detailed manual text analysis of samples of each data-set to identify key linguistic characteristics. Corpus software will then be used to study language patterns in more detail, to determine key patterns of meaning and use in each corpus, and differences between them.

Our findings will be important for professionals concerned with communicating science to the general public, especially young people, including scientists and science journalists. They will also be important for science education professionals, and for organisations concerned with public awareness of climate science.