Building Capacity for Data-Driven Governance: Creating a New Foundation for Democracy.
Existing data flows at the local level, public and administrative records, geospatial data, social media, and surveys are ubiquitous in our everyday life. The Community Learning Data-Driven Discovery (CLD3) process liberates, integrates, and makes these data available to government leaders and resea...
| Publicado en: | Statistics & Public Policy Vol. 4; no. 1; pp. 1 - 12 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=126867249&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 126867249 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 2330443X GQIB jtl: Statistics & Public Policy issn: 2330443X maglogo: N pubinfo: dt: 2017 vid: 4 iid: 1 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 126867249 10.1080/2330443X.2017.1374897 ppf: 1 ppct: 11 formats: tig: atl: Building Capacity for Data-Driven Governance: Creating a New Foundation for Democracy. aug: au: Keller, Sallie Lancaster, Vicki Shipp, Stephanie affil: Social and Decision Analytics Laboratory (SDAL), Biocomplexity Institute of Virginia Tech, Arlington, VA su: United States Educational intervention Education policy Democracy Land grant institutions Statistical learning Data flow computing Geospatial data United States education system sug: subj: Educational intervention Education policy Democracy United States Administration of Education Programs Other provincial and territorial public administration Other local, municipal and regional public administration Land grant institutions Statistical learning Data flow computing Geospatial data United States education system keyword: Administrative data Evaluation Geospatial learning Vulnerable populations Administrative data Evaluation Geospatial learning Vulnerable populations ab: Existing data flows at the local level, public and administrative records, geospatial data, social media, and surveys are ubiquitous in our everyday life. The Community Learning Data-Driven Discovery (CLD3) process liberates, integrates, and makes these data available to government leaders and researchers to tell their community's story. These narratives can be used to build an equitable and sustainable social transformation within and across communities to address their most pressing needs. CLD3 is scalable to every city and county across the United States through an existing infrastructure maintained by collaboration between U.S. Public and Land Grant Universities and federal, state, and local governments. The CLD3 process starts with asking local leaders to identify questions they cannot answer and the potential data sources that may provide insights. The data sources are profiled, cleaned, transformed, linked, and translated into a narrative using statistical and geospatial learning along with the communities' collective knowledge. These insights are used to inform policy decisions and to develop, deploy, and evaluate intervention strategies based on scientifically based principles. CLD3 is a continuous, sustainable, and controlled feedback loop. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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