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Title: Data Science for Public Policy: Of the people, for the people, by the people 2.0 ?

Abstract

In this paper, we explore the role of data science in the public policy lifecycle. We posit policy documents (bills, acts, regulations and directives) as forms of social objects and present a methodology to understand interactions between prior context in professional and personal social networks to a given public policy document release. We employ natural language processing tools along with recent advances in semantic reasoning to formulate document-level proximity metrics which we use to predict the relevance (and impact) of the policy artifacts. These metrics serve as a measure of excitation between people and the public policy initiatives.

Authors:
 [1];  [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1156759
DOE Contract Number:
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: ACM SIG Knowledge Discovery and Data Mining : Workshop on Data Science for Social Good,20140824, 20140827, New York City, NY, NY, USA,
Country of Publication:
United States
Language:
English
Subject:
semantic reasoning

Citation Formats

Sukumar, Sreenivas R., and Shankar, Mallikarjun. Data Science for Public Policy: Of the people, for the people, by the people 2.0 ?. United States: N. p., 2014. Web.
Sukumar, Sreenivas R., & Shankar, Mallikarjun. Data Science for Public Policy: Of the people, for the people, by the people 2.0 ?. United States.
Sukumar, Sreenivas R., and Shankar, Mallikarjun. Wed . "Data Science for Public Policy: Of the people, for the people, by the people 2.0 ?". United States. doi:.
@article{osti_1156759,
title = {Data Science for Public Policy: Of the people, for the people, by the people 2.0 ?},
author = {Sukumar, Sreenivas R. and Shankar, Mallikarjun},
abstractNote = {In this paper, we explore the role of data science in the public policy lifecycle. We posit policy documents (bills, acts, regulations and directives) as forms of social objects and present a methodology to understand interactions between prior context in professional and personal social networks to a given public policy document release. We employ natural language processing tools along with recent advances in semantic reasoning to formulate document-level proximity metrics which we use to predict the relevance (and impact) of the policy artifacts. These metrics serve as a measure of excitation between people and the public policy initiatives.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Jan 01 00:00:00 EST 2014},
month = {Wed Jan 01 00:00:00 EST 2014}
}

Conference:
Other availability
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