Abstract
Social service organizations have long used data in their efforts to support people in need for the purposes of advocacy, tracking, and intervention. Increasingly, such organizations are joining forces to provide wrap-around services to clients in order to “move the needle” on intractable social problems. Groups using these strategies, called Collective Impact, develop shared metrics to guide their work, sharing data, finances, infrastructure, and services. A major emphasis of these efforts is on tracking clients and measuring impacts. This study explores a particular type of Collective Impact strategy called Promise Neighborhoods. Based on a federal grant program, these initiatives attempt to close the achievement gap in particular geographic communities. Through an analysis of publicly available documents and information, the study analyzes the ways these strategies enact (and fail to enact) a collective intelligence for the common good. The analysis focuses specifically on issues surrounding data collection and use, youth agency, leadership and governance, and funding streams. Together, these foci develop a story of an increasingly used “intelligence” with a limited sense of “collective” and a narrow vision of a “common good.” Using this as a platform, the paper explores alternatives that might develop more robust practices around these concepts.
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Notes
For examples, see textbooks like Siegler, Robert S, and Martha Wagner Alibali. Children’s Thinking, 4th Edition. Upper Saddle River, NJ: Prentice Hall, 2005. These lean heavily on epistemological-developmental theories like those of Piaget.
G. Stanley Hall’s two volume text Adolescence effectively defined the field.
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Acknowledgements
I would like to thank the Critical Data Studies Fellowship of the Institute for Advanced Studies at the University of Minnesota for its support to develop the research used in this project. I would also like to thank Douglas Schuler for his attentive, patient, and thorough encouragement and editing.
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Fink, A. Bigger data, less wisdom: the need for more inclusive collective intelligence in social service provision. AI & Soc 33, 61–70 (2018). https://doi.org/10.1007/s00146-017-0719-2
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DOI: https://doi.org/10.1007/s00146-017-0719-2