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Datenauswertungen zur Vorhersage von Entwicklungen – Predictive Risk Modelling

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Bastian, P. Datenauswertungen zur Vorhersage von Entwicklungen – Predictive Risk Modelling. Sozialer Fortschritt, 72(11), 849-868. https://doi.org/10.3790/sfo.72.11.849
Bastian, Pascal "Datenauswertungen zur Vorhersage von Entwicklungen – Predictive Risk Modelling" Sozialer Fortschritt 72.11, 2023, 849-868. https://doi.org/10.3790/sfo.72.11.849
Bastian, Pascal (2023): Datenauswertungen zur Vorhersage von Entwicklungen – Predictive Risk Modelling, in: Sozialer Fortschritt, vol. 72, iss. 11, 849-868, [online] https://doi.org/10.3790/sfo.72.11.849
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Datenauswertungen zur Vorhersage von Entwicklungen – Predictive Risk Modelling

Bastian, Pascal

Sozialer Fortschritt, Vol. 72 (2023), Iss. 11 : pp. 849–868

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Duncker & Humblot
DOI
https://doi.org/10.3790/sfo.72.11.849
Language
German
Pages
20
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Author Details

Bastian, Prof. Dr. Pascal, Rheinland-Pfälzische Technische Universität (RPTU) Kaiserslautern Landau, Fachbereich Erziehungswissenschaften, Arbeitsbereich Sozialpädagogik, Bürgerstraße 23, 76829 Landau.

References

  1. Abbott, A. (1988): The System of Professions, Chicago.  Google Scholar
  2. Ægisdóttir, S./White, M. J./Spengler, P. M./Maugherman, A. S./Anderson, L. A./Cook, R. S./Rush, J. D. (2006): The Meta-Analysis of Clinical Judgment Project. Fifty-Six Years of Accumulated Research on Clinical Versus Statistical Prediction, The Counseling Psychologist, 34(3): S. 341–382. DOI: 10.1177/0011000005285875.  Google Scholar
  3. Akin, B./McDonald, T./Tullis, L. (2010); An inventory of risk assessment in child protection: Instrument usage and key features. Protecting Children, 25(3): 35–51.  Google Scholar
  4. Ayre, P. (2013): Understanding Professional Decisions. Invited Comment on The Impact of Media Reporting of High‐Profile Cases on Child Protection Medical Assessments by Ray et al. (Child Abuse Review 22: 20–24. DOI:10.1002/car.2214), Child Abuse Review, 22(1): S. 25–28. DOI: 10.1002/car.2247.  Google Scholar
  5. Baird, C./Rycus, J. S. (2005): The contribution of decision theory to promoting child safety, APSAC Advisor, 16(4), 17(1): S. 2–10.  Google Scholar
  6. Baird, C./Wagner, D. (2000): The relative validity of actuarial- and consensus-based risk assessment systems. Children and Youth Services Review, 22(11–12): 839–871.  Google Scholar
  7. Bartelink, C./van Yperen, T. A./ten Berge, I. J./de Kwaadsteniet, L./Witteman, C. L. M. (2014): Agreement on Child Maltreatment Decisions. A Nonrandomized Study on the Effects of Structured Decision-Making, Child & Youth Care Forum, 43(5): S. 639–654. DOI: 10.1007/s10566-014-9259-9.  Google Scholar
  8. Bastian, P. (2012): Die Überlegenheit statistischer Urteilsbildung im Kinderschutz – Plädoyer für einen Perspektivwechsel hin zu einer angemessenen Form sozialpädagogischer Diagnosen, in:Marthaler, T./Bastian, P./Bode, I./Schrödter, M. (Hsrg.): Rationalitäten des Kinderschutzes, Wiesbaden, S. 249–267.  Google Scholar
  9. Bastian, P. (2014a): Der praktische Vollzug professioneller Urteilsbildung im Kinderschutz zwischen Interpretation und Klassifikation – Empirische Einblicke, in: Bühler-Niederberger, D./Alberth, L./Eisentraut, S. (Hrsg.): Kinderschutz. Wie kindzentriert sind Programme, Praktiken, Perspektiven?, Weinheim, S. 136–152.  Google Scholar
  10. Bastian, P. (2014b): Statistisch Urteilen – professionell Handeln. Überlegungen zu einem (scheinbaren) Widerspruch, Zeitschrift für Sozialpädagogik, 12(2): S. 145–164. DOI: 12201402145.  Google Scholar
  11. Bastian, P. (2017): Negotiations with a risk assessment tool. Standardized decision-making in the United States and the deprofessionalization thesis, Transnational Social Review, 7(2): S. 206–218. DOI: 10.1080/21931674.2017.1313509.  Google Scholar
  12. Bastian, P. (2019). Sozialpädagogische Entscheidungen. Professionelle Urteilsbildung in der Sozialen Arbeit. Opladen: Barbara Budrich, UTB GmbH.  Google Scholar
  13. Bastian, P./Benoit, M./Freres, K./Posmek, J. (2023): Relationaler Umgang mit Krisen von Jugendamtsfachkräften in der Fallarbeit hinsichtlich der Auswirkungen der Covid-19-Pandemie, Zeitschrift für Erziehungswissenschaft, 26(1): S. 243–263. DOI: 10.1007/s11618-023-01143-1.  Google Scholar
  14. Bastian, P./Freres, K./Schrödter, M. (2017): Risiko und Sicherheit als Orientierung im Kinderschutz. Deutschland und USA im Vergleich, Soziale Passagen, 22(11–12): S. 1–17. DOI: 10.1007/s12592-017-0277-y.  Google Scholar
  15. Bastian, P./Schrödter, M. (2015a): Fachliche Einschätzung bei Verdacht auf Kindeswohlgefährdung, neue praxis, 45(3): S. 224–242.  Google Scholar
  16. Bastian, P./Schrödter, M. (2015b): Risikotechnologien in der professionellen Urteilsbildung der Sozialen Arbeit, in: Kutscher, N./Ley, T./Seelmeyer, U. (Hrsg.), Mediatisierung (in) der Sozialen Arbeit, Baltmannsweiler, S. 192–207).  Google Scholar
  17. Bastian, P./Schrödter, M. (2019): Risikodiagnostik durch „Big Data Analytics“ im Kinderschutz, ARCHIV für Wissenschaft und Praxis der sozialen Arbeit, (2): S. 40–49.  Google Scholar
  18. Broadhurst, K./Hall, C./Wastell, D./White, S./Pithouse, A. (2010): Risk, Instrumentalism and the Humane Project in Social Work: Identifying the Informal Logics of Risk Management in Children’s Statutory Services, British Journal of Social Work, 40(4): S. 1046–1064, DOI: 10.1093/bjsw/bcq011.  Google Scholar
  19. Brown, A./Chouldechova, A./Putnam-Hornstein, E./Tobin, A./Vaithianathan, R. (2019): Toward Algorithmic Accountability in Public Services. Paper presented at the Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems.  Google Scholar
  20. Clark, C. (2005): The Deprofessionalisation Thesis, Accountability and Professional Character, Social Work & Society, 3(2): S. 182–190.  Google Scholar
  21. Coohey, C./Johnson, K./Renner, L. M./Easton, S. D. (2013): Actuarial risk assessment in child protective services: Construction methodology and performance criteria. Children and Youth Services Review, 35(1): S. 151–161. doi: 10.1016/j.childyouth.2012.09.020  Google Scholar
  22. Dahmen, S. (2020): Risikoeinschätzungsinstrumente im Kinderschutz, Sozial Extra, 45(1): S. 36–41, DOI: 10.1007/s12054-020-00349-5.  Google Scholar
  23. Dobner, S./Rasmusson, M. (2015): Social, ethical and legal aspects of Big Data and urban decision, https://www.zsi.at/object/publication/3777/attach/D2_4_Social__Ethical_and_Legal_Aspects_of_Big_Data_and_Urban_Decision_Making.pdf.  Google Scholar
  24. Dollinger, B. (2014): Soziale Arbeit als Realisierung protektiver Sicherheitspolitiken, Zeitschrift für Sozialpädagogik, 12(3): S. 296–314. DOI: 10.3262/ZFSP1403296.  Google Scholar
  25. Drake, B./Jonson-Reid, M./Ocampo, M. G./Morrison, M./Dvalishvili, D. D. (2020): A Practical Framework for Considering the Use of Predictive Risk Modeling in Child Welfare, Ann Am Acad Pol Soc Sci, 692(1): S. 162–181. DOI: 10.1177/0002716220978200.  Google Scholar
  26. Evans, T./Harris, J. (2004): Street-Level Bureaucracy, Social Work and the (Exaggerated) Death of Discretion, British Journal of Social Work, 34(6): S. 871–895. DOI: 10.1093/bjsw/bch106.  Google Scholar
  27. Ferguson, A. G. (2015): Big Data and Predictive Reasonable Suspicion, University of Pennsylvania Law Review, 163(2): S. 327–410.  Google Scholar
  28. Ghanem, C./Eckl, M./Lehmann, R. (2022): Big Data und Forschungsethik in der Sozialen Arbeit, EthikJournal, 8(1), S. 1–18.  Google Scholar
  29. Gillingham, P. (2016): Predictive Risk Modelling to Prevent Child Maltreatment: Insights and Implications from Aotearoa/New Zealand, Journal of Public Child Welfare, 11(2): S. 150–165. DOI: 10.1080/15548732.2016.1255697.  Google Scholar
  30. Gillingham, P. (2019): Can Predictive Algorithms Assist Decision‐Making in Social Work with Children and Families? Child Abuse Review, 28(2): S. 114–126. DOI: 10.1002/car.2547.  Google Scholar
  31. Gillingham, P. (2020): Big Data, prädiktive Analytik und Soziale Arbeit, Sozial Extra, 45(1): S. 31–35. DOI: 10.1007/s12054-020-00348-6.  Google Scholar
  32. Gillingham, P./Graham, T. (2017): Big Data in Social Welfare: The Development of a Critical Perspective on Social Work’s Latest „Electronic Turn“, Australian Social Work, 70(2): S. 135–147. DOI: 10.1080/0312407X.2015.1134606.  Google Scholar
  33. Gillingham, P./Harnett, P./Healy, K./Lynch, D./Tower, M. (2017): Decision Making in Child and Family Welfare: The Role of Tools and Practice Frameworks. Children Australia, 42(1): 49–56. doi: 10.1017/cha.2016.51.  Google Scholar
  34. Gillingham, P./Humphreys, C. (2010): Child Protection Practitioners and Decision-Making Tools: Observations and Reflections from the Front Line, British Journal of Social Work, 40(8): S. 2598–2616. DOI: 10.1093/bjsw/bcp155.  Google Scholar
  35. Grove, W. M./Zald, D. H./Lebow, B. S./Snitz, B. E./Nelson, C. (2000): Clinical versus mechanical prediction: A meta-analysis, Psychological Assessment, 12(1): S. 19–30.  Google Scholar
  36. Gutwald, R./Burghardt, J./Kraus, M./Reder, M./Lehmann, R./Müller, N. (2021): Soziale Konflikte und Digitalisierung. Chancen und Risiken digitaler Technologien bei der Einschätzung von Kindeswohlgefährdungen, EthikJournal, 7(2), S. 1–20.  Google Scholar
  37. Hanson, R. K./Morton-Bourgon, K. E. (2009): The accuracy of recidivism risk assessments for sexual offenders: a meta-analysis of 118 prediction studies, Psychological Assessment, 21(1): 1–21. DOI: 10.1037/a0014421.  Google Scholar
  38. Hay, S. I./George, D. B./Moyes, C. L./Brownstein, J. S. (2013): Big Data Opportunities for Global Infectious Disease Surveillance, PLOS Medicine, 10(4): e1001413. DOI: 10.1371/journal.pmed.1001413.  Google Scholar
  39. Healy, K. (2009): A case of mistaken identity: The social welfare professions and New Public Management, Journal of Sociology, 45(4): S. 401–418. DOI: 10.1177/1440783309346476.  Google Scholar
  40. Heggdalsvik, I. K./Rød, P. A./Heggen, K. (2018): Decision-making in child welfare services: Professional discretion versus standardized templates, Child & Family Social Work, 23(3), S. 522–529 DOI: 10.1111/cfs.12444.  Google Scholar
  41. Høybye-Mortensen, M. (2015): Decision-Making Tools and Their Influence on Caseworkers’ Room for Discretion, British Journal of Social Work, 45(2): S. 600–615. DOI: 10.1093/bjsw/bct144.  Google Scholar
  42. Johnson, W. L. (2011): The validity and utility of the California Family Risk Assessment under practice conditions in the field: A prospective study, Child Abuse and Neglect, 35 (1): S. 18–28.  Google Scholar
  43. Johnson, W./Clancy, T./Bastian, P. (2015): Child abuse/neglect risk assessment under field practice conditions: Tests of external and temporal validity and comparison with heart disease prediction, Children and Youth Services Review, 56: S. 76–85. DOI: 10.1016/j.childyouth.2015.06.013.  Google Scholar
  44. Kalliatakis, G./Ehsan, S./McDonald-Maier, K. D. (2017): A Paradigm Shift: Detecting Human Rights Violations Through Web Images. Human Rights Practice in the Digital Age Workshop, https://arxiv.org/abs/1703.10501.DOI:10.48550/arXiv.1703.10501.  Google Scholar
  45. Keddell, E. (2014): The ethics of predictive risk modelling in the Aotearoa/New Zealand child welfare context: Child abuse prevention or neo-liberal tool? Critical Social Policy, 35(1): S. 69–88. DOI: 10.1177/0261018314543224.  Google Scholar
  46. Kemshall, H. (2010): Risk Rationalities in Contemporary Social Work Policy and Practice, British Journal of Social Work, 40(4): S. 1247–1262. DOI: 10.1093/bjsw/bcp157.  Google Scholar
  47. Klatetzki, T. (2020): Der Umgang mit Fehlern im Kinderschutz. Eine kritische Betrachtung, neue praxis, 20(2): S. 101–121.  Google Scholar
  48. Krakouer, J./Wu Tan, W./Parolini, A. (2021): Who is analysing what? The opportunities, risks and implications of using predictive risk modelling with Indigenous Australians in child protection: A scoping review, Australian Journal of Social Issues, 56(2): S. 173–197. DOI: 10.1002/ajs4.155.  Google Scholar
  49. Kunstreich, T. (2003): Neo-Diagnostik – Modernisierung klinischer Professionalität, Widersprüche, (88): S. 7–10.  Google Scholar
  50. Kunstreich, T./Langhanky, M./Lindenberg, M./May, M. (2004): Dialog statt Diagnose, in: Heiner, M. (Hrsg.): Diagnostik und Diagnosen in der Sozialen Arbeit. Ein Handbuch, Gelsenkirchen, S. 26–39.  Google Scholar
  51. Latour, B. (2017): Eine neue Soziologie für eine neue Gesellschaf: Einführung in die Akteur-Netzwerk-Theorie, Frankfurt/M.  Google Scholar
  52. Lipsky, M. (2010): Street-Level Bureaucracy, New York.  Google Scholar
  53. Luhmann, N./Schorr, K.-E. (1979). Reflexionsprobleme im Erziehungssystem. Stuttgart: Klett-Cotta.  Google Scholar
  54. Masson, H./Frost, N./Parton, N. (2008): Reflections from the ‚frontline‘: social workers’ experiences of post-qualifying child care training and their current work practices in the new children’s services, Journal of Children’s Services, 3(3): S. 54–64.  Google Scholar
  55. McNeece, C. A./Thyer, B. A. (2004): Evidence-Based Practice and Social Work. Journal of Evidence-Based Social Work, 1(1): S. 7–25. doi: 10.1300/J394v01n01_02.  Google Scholar
  56. Ministry of Social Development (2014): Final report on feasibility of using predictive risk modelling, Wellington.  Google Scholar
  57. Mittelstadt, B. D./Floridi, L. (2016): The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts, Sci Eng Ethics, 22(2): S. 303–341. DOI: 10.1007/s11948-015-9652-2.  Google Scholar
  58. Müller, B. (2006): Sozialpädagogische Diagnose, in: Galuske, M./Thole, W. (Hrsg.): Vom Fall zum Mangement, Wiesbaden, S. 83–96.  Google Scholar
  59. Munro, E./Hardie, J. (2018): Why We Should Stop Talking About Objectivity and Subjectivity in Social Work, The British Journal of Social Work, 49(2): S. 411–427. DOI: 10.1093/bjsw/bcy054.  Google Scholar
  60. Niño, M./Zicari, R. V./Ivanov, T./Hee, K./Mushtaq, N./Rosselli, M./Underwood, H. (2017): ‚Data Projects for „Social Good“: Challenges and Opportunities’, International Journal of Humanities and Social Sciences, 5(11): S. 1094 –1104. DOI 10.5281/zenodo.1130095.  Google Scholar
  61. Oak, E. (2015): A Minority Report for Social Work? The Predictive Risk Model (PRM) and the Tuituia Assessment Framework in addressing the needs of New Zealand’s Vulnerable Children, The British Journal of Social Work, 46(5): S. 1208–1223. DOI: 10.1093/bjsw/bcv028.  Google Scholar
  62. Oevermann, U. (1996): Theoretische Skizze einer revidierten Theorie professionalisierten Handelns, in: Combe, A./Helsper, W. (Hrsg.): Pädagogische Professionalität. Untersuchungen zum Typus pädagogischen Handelns, Frankfurt am Main, S. 70–182).  Google Scholar
  63. Palanisamy, V./Thirunavukarasu, R. (2019): Implications of big data analytics in developing healthcare frameworks – A review, Journal of King Saud University – Computer and Information Sciences, 31(4): S. 415–425. DOI: https://doi.org/10.1016/j.jksuci.2017.12.007.  Google Scholar
  64. Parton, N. (2009): Challenges to practice and knowledge in child welfare social work: From the ‚social‘ to the ‚informational‘? Children and Youth Services Review, 31(7): S. 715–721. DOI: 10.1016/j.childyouth.2009.01.008.  Google Scholar
  65. Phillips, L./Dowling, C. P./Shaffer, K./Hodas, N. O./Volkova, S. (2017): Using Social Media to Predict the Future: A Systematic Literature Review. ArXiv, abs/1706.06134.  Google Scholar
  66. Pollack, S. (2008): Labelling Clients ‚Risky‘: Social Work and the Neo-liberal Welfare State, The British Journal of Social Work, 40(4): S. 1263–1278. DOI: 10.1093/bjsw/bcn079.  Google Scholar
  67. Ponnert, L./Svensson, K. (2016): Standardisation – the end of professional discretion? European Journal of Social Work, 19(3–4): S. 586–599. DOI: 10.1080/13691457.2015.1074551.  Google Scholar
  68. Posmek, J./Bastian, P. (2022): Die Zirkulation von Fluchtnarrationen. Über die Erzählungen von Fluchtwegen und deren Thematisierung in sozialpädagogischen Beratungskontexten, Zeitschrift für erziehungswissenschaftliche Migrationsforschung (ZeM), 1(1): S. 59–73. DOI: 10.3224/zem.v1i1.05.  Google Scholar
  69. Sawyer, J. (1966): Measurement and prediction, clinical and statistical, Psychological bulletin, 66(3): S. 178–200.  Google Scholar
  70. Schneider, D. (2021): Ein Schritt in Richtung De-Professionalisierung? Plädoyer für eine intensive Diskussion über algorithmische Systeme in der professionellen Praxis, in: Wunder, M. (Hrsg.): Digitalisierung und Soziale Arbeit. Transformationen und Herausforderungen. Bad Heilbrunn, S. 122–139.  Google Scholar
  71. Schneider, D./Seelmeyer, U. (2018): Der Einfluss der Algorithmen, Sozial Extra, 42(3): S. 21–24. DOI: 10.1007/s12054-018-0046-y.  Google Scholar
  72. Schneider, D./Seelmeyer, U. (2019): Challenges in Using Big Data to Develop Decision Support Systems for Social Work in Germany, Journal of Technology in Human Services, 37(2–3): S. 1–16. DOI: 10.1080/15228835.2019.1614513.  Google Scholar
  73. Schrödter, M. (2006): Diagnose und Profession, Sozial Extra, 30(10): S. 8–9.  Google Scholar
  74. Schrödter, M. (2020): Bedingungslose Jugendhilfe, Wiesbaden.  Google Scholar
  75. Schrödter, M./Bastian, P./Taylor, B. (2018): Risikodiagnostik in der Sozialen Arbeit an der Schwelle zum „digitalen Zeitalter“ von Big Data Analytics, Preprint: S. 1–16, https://www.researchgate.net/publication/323267949\_Risikodiagnostik\_in\_der\_Sozialen\_Arbeit\_an\_der\_Schwelle\_zum\_digitalen\_Zeitalter\_von\_Big\_Data\_Analytics, zuletzt geprüft am [15.6.2023].  Google Scholar
  76. Schrödter, M./Bastian, P./Taylor, B. (2019): Risikodiagnostik und Big Data Analytics in der Sozialen Arbeit, in: Kutscher, N./Ley, T./Seelmeyer, U./Siller, F./Tillmann, A./Zorn, I. (Hrsg.): Handbuch Soziale Arbeit und Digitalisierung. Weinheim, S. 255–264.  Google Scholar
  77. Schrödter, M./Bastian, P./Taylor, B. (2020): Risikodiagnostik und Big Data Analytics in der Sozialen Arbeit, in:Kutscher, N./Ley, T./Seelmeyer, U./Siller, F./Tillmann, A./Zorn, I. (Hrsg): Handbuch Soziale Arbeit und Digitalisierung, Weinheim, S. 255–264).  Google Scholar
  78. Seelmeyer, U. (2020): Big Data & Künstliche Intelligenz – Neue Anforderungen an die Fachlichkeit in sozialen Berufen, Blätter der Wohlfahrtspflege, 167(3): S. 95–98. DOI: 10.5771/0340-8574-2020-3-95.  Google Scholar
  79. Sletten, M. S./Ellingsen, I. T. (2020): When standardization becomes the lens of professional practice in child welfare services, Child & Family Social Work, 25(3): S. 714–722. DOI: 10.1111/cfs.12748.  Google Scholar
  80. Spratt, T. (2001): The Influence of Child Protection Orientation on Child Welfare Practice, British Journal of Social Work, 31(6): S. 933–954. DOI: 10.1093/bjsw/31.6.933.  Google Scholar
  81. Stanford, S. (2009): ‚Speaking Back‘ to Fear: Responding to the Moral Dilemmas of Risk in Social Work Practice, British Journal of Social Work, 40(4): bcp156-1080. DOI: 10.1093/bjsw/bcp156.  Google Scholar
  82. Strobel, B./Liel, C./Kindler, H. (2008): Validierung und Evaluierung des Kinderschutzbogens, Ergebnisbericht München, DJI.  Google Scholar
  83. Thompson, L. J./Wadley, D. A. (2018): Countering globalisation and managerialism: Relationist ethics in social work, International Social Work, 61(5): S. 706–723. DOI: 10.1177/0020872816655867.  Google Scholar
  84. Turba, H. (2012): Grenzen „begrenzter Rationalität“ – Politisch-administrative Steuerungsambitionen im Kinderschutz, in: Marthaler, T./Bastian, P./Bode, I./Schrödter, M. (Hrsg.): Rationalitäten des Kinderschutzes. Kindeswohl und soziale Interventionen aus pluraler Perspektive, Wiesbaden, S. 79–104.  Google Scholar
  85. Uhlendorff, U. (1997): Sozialpädagogische Diagnosen III. Ein sozialpädagogisch-hermeneutisches Diagnoseverfahren für die Hilfeplanung, Weinheim und München.  Google Scholar
  86. van de Luitgaarden, G. M. J. (2009): Evidence-Based Practice in Social Work: Lessons from Judgment and Decision-Making Theory, British Journal of Social Work, 39(2): S. 243–260. DOI: 10.1093/bjsw/bcm117.  Google Scholar
  87. van der Put, C. E./Assink, M./Boekhout van Solinge, N. F. (2017): Predicting child maltreatment: A meta-analysis of the predictive validity of risk assessment instruments, Child Abuse & Neglect, 73: S. 71–88.  Google Scholar
  88. Vannier Ducasse, H. (2021): Predictive risk modelling and the mistaken equation of socio-economic disadvantage with risk of maltreatment, The British Journal of Social Work, 51(8): S. 3153–3171. DOI: 10.1093/bjsw/bcaa182.  Google Scholar
  89. Webb, S. A. (2009): Risk, Governmentality and Insurance: The Actuarial Re-Casting of Social Work, in: Otto, H. U./Polutta, A./Ziegler, H. (eds.), Evidence-based Practice – Modernising the Knowledge Base of Social Work? Opladen, S. 211–226.  Google Scholar
  90. Williams, M. L./Burnap, P./Sloan, L. (2016): Crime Sensing With Big Data: The Affordances and Limitations of Using Open-source Communications to Estimate Crime Patterns, The British Journal of Criminology, 57(2): S. 320–340. DOI: 10.1093/bjc/azw031.  Google Scholar
  91. Zetino, J./Mendoza, N. (2019): Big Data and Its Utility in Social Work: Learning from the Big Data Revolution in Business and Healthcare, Social Work in Public Health, 34(5): S. 409–417. DOI: 10.1080/19371918.2019.1614508.  Google Scholar
  92. Zielesny, A. (2011): From Curve Fitting to Machine Learning – An Illustrative Guide to Scientific Data Analysis and Computational Intelligence, Berlin/Heidelberg.  Google Scholar

Abstract

Data Evaluation to Predict Developments – Predictive Risk Modelling

In some fields of social work, risk prediction is part of the diagnostic process. Using the example of child protection, in which risk assessment is prescribed by law, the article systematises different procedures based on the collection, weighting and evaluation of data and discusses the effects of different risk prediction tools on professional judgement as well as on the professionals’ scope for discretion. A special focus is placed on recent developments of algorithm-based methods. After an overview of current research and the debate on standardised and algorithm-based procedures, the article discusses the possibilities of integrating such tools into the complex professional judgement and action system.

Table of Contents

Section Title Page Action Price
Pascal Bastian: Datenauswertungen zur Vorhersage von Entwicklungen – Predictive Risk Modelling 849
Zusammenfassung 849
Abstract: Data Evaluation to Predict Developments – Predictive Risk Modelling 849
1. Einführung 850
2. Eine kurze Sortierung einzelner Modelle der Risikoprognoseverfahren 853
2.1 Die Klassiker: Konsensuale und aktuarialistische Risikoprognose 854
2.2 Zukünftige Entwicklungen: algorithmusbasierte Risikoprognose (Big Data Analytics) 855
3. Einblicke in die empirische Forschung und kritische Debatten 856
4. Ausblick: Die Einbindung standardisierter Risikoprognoseverfahren in die professionelle Praxis 860
Literatur 862

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