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  Beck U. (1992). Risk society: towards a new modernity.
  London; Newbury Park, California: Sage Publications.
  Begg C. (2018). Power, responsibility and justice:
  a review of local stakeholder participation in European flood risk management.
  Begg C., Callsen I., Kuhlicke C., Kelman I. (2017a).
  The role of local stakeholder participation in flood defence decisions in the UK and
  Germany. Begg C., Ueberham M., Masson T., Kuhlicke C.
  (2017b). Interactions between citizen responsibilization, flood experience and
  household resilience: insights from the 2013 flood in Germany.
  Int. J. Water Resour. Begg C., Walker G., Kuhlicke C.
  (2015). Localism and flood risk management in England: the creation of
  new inequalities? Environ. Benson D., Lorenzoni I., Cook H.
  (2016). Evaluating social learning in England flood risk management: An ‘individual-community interaction’ perspective.
  Benzie M. (2014). Social justice and adaptation in the UK.
  Berrang-Ford L., Siders A. R., Lesnikowski A.,
  Fischer A. P., Callaghan M. W., Haddaway N. R., et al.
  
  
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  A milder version of this theory is simply that we're overrun .
  Most of us take that for granted at this point.
  But I think the sheer volume and scale of what's coming will be meaningfully different.
  And I think we're unprepared. Or at least,
  I am. Our new challenge as little snowflake
  humans will be to prove we aren't language models. It's the reverse .
  After the forest expands, we will become deeply
  sceptical of one another's realness. Every time you find a new favourite blog or Twitter
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  Before you continue, pause and consider:
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  As language models become increasingly capable and impressive, we should remember
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  Based on the procedure described in Section 3.3.2,
  a topic model was generated, which classified
  chat conversations into 16 topics. The top-30 most relevant terms for each topic, as classified by pyLDAVis with a λ value
  of 0.6, are presented in Appendix H. While these terms provide a beginning
  for understanding the contents of each topic, several other metrics were used to interpret the
  results of the topic model. These include: (1) the distribution of topic weight
  scores across documents classified with each topic, (2) the distribution of meaningful key terms across each topic, and (3) qualitative readings of representative and
  randomly selected documents. For each document, the LDA model calculates a topic probability distribution.
  The topic with the highest probability in a given document was identified as that document’s dominant topic.
  Figure 5.3 presents a violin chart showing the distribution of topic weights per
  document for each dominant topic.
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