scholarly journals Introduction to the Special Issue: Data-Driven Approaches to Research and Teaching in Professional and Technical Communication

2018 ◽  
Vol 61 (4) ◽  
pp. 352-355 ◽  
Author(s):  
Ryan K. Boettger ◽  
Suguru Ishizaki
Keyword(s):  
Technical Communication ◽  
Data Driven ◽  
Special Issue ◽  
Research And Teaching ◽  
Professional And Technical Communication

Special issue on dynamic depth field data driven learning, recognition and computation

2017 ◽  
Vol 252 ◽  
pp. 1-2
Author(s):  
Qiong Liu ◽  
Yang Li ◽  
Le An ◽  
Yi Zhen
Keyword(s):  
Field Data ◽  
Data Driven ◽  
Special Issue

An Episode in the History of PreCrime

2018 ◽  
Vol 48 (5) ◽  
pp. 637-647
Author(s):  
Rebecca Lemov
Keyword(s):  
Mathematical Models ◽  
Crime Prevention ◽  
Scientific Study ◽  
Behavioral Data ◽  
Data Driven ◽  
Special Issue ◽  
Predictive Policing ◽  
The Core ◽  
Current Spread ◽  
History Of

This article traces the rise of “predictive” attitudes to crime prevention. After a brief summary of the current spread of predictive policing based on person-centered and place-centered mathematical models, an episode in the scientific study of future crime is examined. At UCLA between 1969 and 1973, a well-funded “violence center” occasioned great hopes that the quotient of human “dangerousness”—potential violence against other humans—could be quantified and thereby controlled. At the core of the center, under the direction of interrogation expert and psychiatrist Louis Jolyon West, was a project to gather unprecedented amounts of behavioral data and centrally store it to identify emergent crime. Protesters correctly seized on the violence center as a potential site of racially targeted experimentation in psychosurgery and an example of iatrogenic science. Yet the eventual spectacular failure of the center belies an ultimate success: its data-driven vision itself predicted the Philip K. Dick–style PreCrime policing now emerging. The UCLA violence center thus offers an alternative genealogy to predictive policing. This essay is part of a special issue entitled Histories of Data and the Database edited by Soraya de Chadarevian and Theodore M. Porter.

scholarly journals Evaluating Stochastic Seeding Strategies in Networks

2021 ◽  
Author(s):  
Alex Chin ◽  
Dean Eckles ◽  
Johan Ugander
Keyword(s):  
Field Experiments ◽  
Random Network ◽  
Data Driven ◽  
Large Field ◽  
Special Issue ◽  
Valid Inference ◽  
Nonparametric Estimators ◽  
Prescriptive Analytics ◽  
Marketing Intervention ◽  
Random Individual

When trying to maximize the adoption of a behavior in a population connected by a social network, it is common to strategize about where in the network to seed the behavior, often with an element of randomness. Selecting seeds uniformly at random is a basic but compelling strategy in that it distributes seeds broadly throughout the network. A more sophisticated stochastic strategy, one-hop targeting, is to select random network neighbors of random individuals; this exploits a version of the friendship paradox, whereby the friend of a random individual is expected to have more friends than a random individual, with the hope that seeding a behavior at more connected individuals leads to more adoption. Many seeding strategies have been proposed, but empirical evaluations have demanded large field experiments designed specifically for this purpose and have yielded relatively imprecise comparisons of strategies. Here we show how stochastic seeding strategies can be evaluated more efficiently in such experiments, how they can be evaluated “off-policy” using existing data arising from experiments designed for other purposes, and how to design more efficient experiments. In particular, we consider contrasts between stochastic seeding strategies and analyze nonparametric estimators adapted from policy evaluation and importance sampling. We use simulations on real networks to show that the proposed estimators and designs can substantially increase precision while yielding valid inference. We then apply our proposed estimators to two field experiments, one that assigned households to an intensive marketing intervention and one that assigned students to an antibullying intervention. This paper was accepted by Gui Liberali, Special Issue on Data-Driven Prescriptive Analytics.

Call for Special Issue Papers: Big Data-Driven Futuristic Fabric System in Societal Digital Transformation

Big Data ◽  
2021 ◽  
Author(s):  
Dr. Chinmay Chakraborty ◽  
Prof. Muhammad Khurram Khan ◽  
Prof. Ishfaq Ahmad
Keyword(s):  
Big Data ◽  
Digital Transformation ◽  
Data Driven ◽  
Special Issue ◽  
Fabric System

Call for Special Issue Papers: Big Data-Driven Futuristic Fabric System in Societal Digital Transformation

Big Data ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 151-152
Author(s):  
Chinmay Chakraborty ◽  
Muhammad Khurram Khan ◽  
Ishfaq Ahmad
Keyword(s):  
Big Data ◽  
Digital Transformation ◽  
Data Driven ◽  
Special Issue ◽  
Fabric System

Reflections on Research in Kinesiology

2021 ◽  
pp. 1-7
Author(s):  
Karl M. Newell
Keyword(s):  
Physical Activity ◽  
Research Agenda ◽  
Healthy Lifestyle ◽  
Human Movement ◽  
Current Status ◽  
Special Issue ◽  
New Developments ◽  
Related Research ◽  
Research And Teaching ◽  
Initial Research

This paper provides reflections on the progress to date and current status of research in kinesiology. The accompanying overview articles in this special issue of Kinesiology Review show that the contemporary disciplinary/professional foci of kinesiology remain, by and large, the same as the initial research and teaching structures of 50 years ago, as outlined in the inaugural overviews. Nevertheless, within this prevailing disciplinary/professional structure, there have been many new developments in movement-related research, including the juxtaposition of novel alignments and integrations of certain specializations of kinesiology. There is general consensus that the quality and quantity of research in kinesiology have advanced substantially, albeit unevenly, on multiple fronts, both within and between the areas of specialization. The research agenda in kinesiology has benefitted from the growing realization of the centrality of human movement and physical activity in contributing to a healthy lifestyle for individuals and societies.

Call for Papers—Management Science—Special Issue on Data-Driven Prescriptive Analytics

2018 ◽  
Vol 64 (6) ◽  
pp. 2972-2972
Author(s):  
Kay Giesecke ◽  
Gui Liberali ◽  
Hamid Nazerzadeh ◽  
J. George Shanthikumar ◽  
Chung Piaw Teo
Keyword(s):  
Management Science ◽  
Data Driven ◽  
Special Issue ◽  
Prescriptive Analytics
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