Risk Literacy

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Risk Literacy is understood as the ability to perceive the risk individuals, communities and environment is exposed to and a derive appropriate decision from the awarness about the risk. The term's meaning has been expanded to include the ability to use detect actively the risk, identify risk mitigation resources and other means to understand and use these resources. Risk perception is the subjective judgment people make about the severity and/or probability of a risk, and may vary person to person. Any human endeavor carries some risk, but some are much riskier than others.[1]

Risk and Response[edit]

Basic Risk and Response Cycle

Risk Literacy is understood as the ability to

  • (Risk) perceive and process risks (Risk Awareness)and
  • (Response) perform activities of risk mitigation.

If we consider risk as:

risk awareness might refer to the probability and/or the impact of events for the exposed population. Citizens might regard a risk as not high because the probability is not high (e.g. accident in an atomic power plant) or they might be very afraid of the risk because they aware of the huge impact on society, long-term contamination of areas and the impact on public health. Scientific assessment of risk literacy involves both

  • the comprehension about the probability and
  • the comprehension about the impact.

Subtopics[edit]

Learning Task[edit]

  • (Risk Literacy - Psychology) Gerd Gigerenzer worked on the scientific area of Risk Communication from the perspective of Psychology. Explore the literature of Gerd Gigerenzer and enhance this article with scientific evidence for psychological experimental designs that provide insights in risk communication and risk awareness.
  • (Risk Literacy Test) Perform the Risk Literacy Test available at the Harding Centre for Risk Literacy to explore some of your own skills.
  • (Spatial Risk Literacy): Analyse at OpenLayers HeatMap for Earthquake[2], spatial aspects of risk are visualized and how the visualisation is based on collected data.
  • (Mobile Devices as Decision Support Client) To become risk literate is it necessary to deal with data, information and knowledge about the events and environments that expose citizens to a certain risk. Digital infrastructure (mobile devices, web-service, ...) can be used as an important resource of information about risk and available resources for risk mitigation. Explore the concept of Augmented Reality and apply that for visualize invisible risks,
    • like contanimation of water resources (see [[Wikipedia:Ecotoxicology]),
    • epidemiological risks (see Wikipedia:Public Health),
    • risks about public safety in general (e.g. DOs and DONTs, emergency numbers, ...)
  • (Trust in Open Educational Resources for Risk Literacy) The trust in those capacity building resources for risk literacy is a key element for citizens or decision makers in general. It is recommended to explore the learning resource about the concept of digital signature to understand, how trust in digital information can supported.
  • (Systems Thinking and SDGs) A developing country introduces wet rice to accomplish the Sustainable Development Goal 2: "Zero Hunger". Malaria cases were nearly eliminated. Since introduction of wet rice the Malaria cases increased. So the positive impact on SDG2 has an negative impact on Sustainable Development Goal 3: "Good Health and Well-being". Explain why Systems Thinking would be helpful for systems analysis and problem solving!

See also[edit]

Suggested ToDos for Author[edit]

  • Decompose the resource into subresources with own learning tasks
  • Test design for Risk Literacy - add topic, including computer-adaptive test (see Concerto - University of Cambridge[3]

References[edit]

  1. Hansson, Sven Ove; Zalta, Edward N. (Spring 2014). "Risk". The Stanford Encyclopedia of Philosophy. Retrieved 9 May 2014. 
  2. OpenLayers - webbased framework to visualize maps - https://openlayers.org
  3. Magis, D., & Barrada, J. R. (2017). Computerized adaptive testing with R: Recent updates of the package catR. Journal of Statistical Software, 76(1), 1-19.