PANICOCENE > Research > WP1

Framework Analysis

To identify the representational practices that, during the past 20 years, have shaped printed and online news media discourses relating to climate change-induced mobilities across a set of specific disaster milestones.

How has the representation of climate change-induced mobilities developed in news media narratives and discourses over the past 20 years?

  • 1.1. Which major milestones can be identified as turning points in news media discourses on the topic? Given the exacerbation of the COVID-19 pandemic during recent years, a cross-cutting concern would be to explore the likely impact of the pandemic on climate change-induced mobilities-related narratives and discourses.

Methodology: The goal of WP1, through ‘quick-and-dirty keyword search’ and semi-structured interviews, is to identify major disaster milestones and capture key elements of climate change-induced mobilities framing emerging in media narratives.

  • Desk review and analysis will help enhance and fine tune the research design in order to achieve better outcomes in terms of research outputs. This is expected to be an ongoing process, partially stemming also from previous personal research work.
  • Thanks to initiatives carried out at my host Institute, it will be possible to scan the CIESIN database analysis and identify an initial set of key mobilities-focused environmental disasters to depart from.
  • A ‘quick-and-dirty keyword search’ will help identify the major mobilities-linked climate disasters that have received primary news media attention.
  • In addition, semi-structured interviews will be carried out with journalists, activists, artivistis, researchers and sensitized professionals, to delve deeper into some of the environmental disasters and framing prioritized in the media. This might help to understand the relationship between journalism, academia and activism.

Finally, a cross-analysis will seek to match major environmental disasters in terms of intensity and destructive impact (database analysis) with those that received higher news media attention (‘quick-and dirty keyword search’ and semi-structured interviews) in order to identify milestones, based on which further framing the corpus for analysis.