Abstract
In the final chapter of the book, the author explores the ethics involved in the communication of crime statistics and the issues and challenges that policy makers, criminologist, statisticians and journalists face in providing a more comprehensive coverage to better inform the public. The chapter discusses how crime statistics can be abused by both government officials and journalists in order to advance certain agendas and why this is so problematic. It examines specific cases in the light of professional ethics and discusses approaches and strategies to deal with these issues while analysing the challenges posed by big data and datafication in an increasingly changing media landscape.
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Notes
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Rawlinson, Kevin (2016). Sun ordered to admit British Muslims story was ‘significantly misleading’. The Guardian . https://www.theguardian.com/media/2016/mar/26/ipso-sun-print-statement-british-muslims-headline [Accessed on 11 February 2017].
References
Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Cambridge, MA: National Bureau of Economic Research.
Brandao, R. F. (2017). The uses of science statistics in the news media and on daily life. In M.-S. RamÃrez-Montoya (Ed.), Handbook of research on driving STEM learning with educational technologies (pp. 506–523). Hershey, PA: IGI Global.
Chan, J., & Bennett Moses, L. (2016). Is big data challenging criminology? Theoretical Criminology, 20(1), 21–39.
Chin, G. (2002). Race, the war on drugs, and the collateral consequences of criminal conviction. Journal of Gender, Race & Justice, 6(1), 255–278.
Crawford, K., & Schultz, J. (2014). Big data and due process: Toward a framework to redress predictive privacy harms. BCL Review, 55, 93–128.
Debreu, G. (1991). The mathematization of economic theory. The American Economic Review, 81(1), 1–7.
Diamond, J. (2017). Trump falsely claims US murder rate is ‘highest’ in 47 years. CNN Online. Retrieved March 12, 2017, from http://edition.cnn.com/2017/02/07/politics/donald-trump-murder-rate-fact-check/index.html.
Dowden, A. R., Ethridge, G., & Brooks, M. (2016). The impact criminal history has on the employability of African American and Latino populations with disabilities receiving state vocational rehabilitation services: Implications for adding a criminal history variable to the RSA-911 data. Journal of Vocational Rehabilitation, 45(2), 213–224.
Dunwoody, S. (2014). Science journalism: Prospects in the digital age. In M. Bucchi & B. Trench (Eds.), Routledge handbook of public communication of science and technology (pp. 27–39). London: Routledge.
Gibbs, S. (2016). US border control could start asking for your social media accounts. Retrieved January 3, 2017, from https://www.theguardian.com/technology/2016/jun/28/us-customs-border-protection-social-media-accounts-facebook-twitter
Herman, E. S., & Chomsky, N. (2010 [1984]). Manufacturing consent: The political economy of the mass media. London: Random House.
Keitel, C., Kotzmann, E., & Skovsmose, O. (1993). Beyond the tunnel vision: Analysing the relationship between mathematics, society and technology. In C. Keitel & K. Ruthven (Eds.), Learning from computers: Mathematics education and technology (pp. 243–279). New York: Springer.
Kucharski, A. (2016). Post-truth: Study epidemiology of fake news. Nature, 540(7634), 525–525.
Macionis, J. J. (2017). Sociology. London: Pearson Higher.
Maier, S. R. (2003). Numeracy in the newsroom: A case study of mathematical competence and confidence. Journalism & Mass Communication Quarterly, 80(4), 921–936.
Miller, J. G. (1996). Search and destroy: African-American males in the criminal justice system. Cambridge: Cambridge University Press.
Nguyen, A., & Lugo-Ocando, J. (2015). Introduction: The state of statistics in journalism and journalism education—Issues and debates. Journalism, 17(1), 3–17.
O’Neil, C. (2016). Weapons of math destruction. How big data increases inequality and threatens democracy. New York: Crown.
Polanyi, K. (1957 [1945]). The great transformation (R. M. MacIver, Trans.). Boston: Beacon Press.
Popper, K. R. (1972). Objective knowledge: An evolutionary approach. Oxford: Oxford University Press.
Popper, K. R. (2002). The poverty of historicism. Abingdon-on-Thames, OX: Psychology Press.
Ruckenstein, M., & Pantzar, M. (2015). Datafied life: Techno-anthropology as a site for exploration and experimentation. Techné: Research in Philosophy and Technology, 19(2), 191–210.
Sampson, R. J. (2013). The place of context: A theory and strategy for criminology’s hard problems. Criminology, 51(1), 1–31.
Skovsmose, O. (2014). Mathematization as social process. In O. Skovsmose (Ed.), Encyclopedia of mathematics education (pp. 441–445). New York: Springer.
Van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society, 12(2), 197–208.
Yang, X. (2016). Analysing datafied life. Ph.D. in Computing, Imperial College London, London. Retrieved from https://spiral.imperial.ac.uk/handle/10044/1/33722
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Lugo-Ocando, J. (2017). Conclusion. In: Crime Statistics in the News. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-137-39841-3_10
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DOI: https://doi.org/10.1057/978-1-137-39841-3_10
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