Broken Chains and Subverted Plans

2017 ◽  
Author(s):  
Christopher C. Fennell
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Selective Advantage ◽  
Commodity Chains ◽  
Structural Racism ◽  
Policy Makers ◽  
Economic Opportunities ◽  
Production And Consumption ◽  
Development Plans ◽  
Anglo American

Broken Chains and Subverted Plans: Ethnicity, Race, and Commodities examines the ways in which the large-scale development plans of Anglo-American governing officials and investors were subverted by the choices of individuals and social networks in the regions of Virginia and Illinois in the nineteenth century. The lessons from this study inform issues very current today, as economists and policy makers debate the best ways to create new markets and develop commodity chains of production and consumption spanning the globe. The backcountry of Virginia presents a story of German-American farmers utilizing ethnic social networks to take selective advantage of economic opportunities promoted by Anglo-American officials and investors. The region of Illinois illustrates the ways in which African Americans worked to overcome the overt and structural racism that shaped the availability of land and economic opportunities in the Midwest. These two case studies emerge from multi-year research projects in which Fennell served as a principal investigator, analyst, and archaeologist.

Introduction

2017 ◽  
Author(s):  
Christopher C. Fennell
Keyword(s):  
Social Networks ◽  
Economic Systems ◽  
Commodity Chains ◽  
Economic Dynamics ◽  
Ethnic Networks ◽  
Global Commodity Chains ◽  
World Economic ◽  
Development Plans ◽  
The Subject

The introduction provides an overview of the themes of world economic systems, global commodity chains, and ways in which development plans can be thwarted by local social networks and ostensibly peripheral players. This chapter opens the subject of the ways in which these theories have neglected the impacts of ethnic networks and racism upon economic dynamics. This critique is revisited and expanded in the concluding chapters seven and eleven.

Elite Strategies, Local Networks

2017 ◽  
Author(s):  
Christopher C. Fennell
Keyword(s):  
Social Networks ◽  
Material Culture ◽  
Group Dynamics ◽  
Spatial Models ◽  
Commodity Chains ◽  
Local Networks ◽  
Group Solidarity ◽  
Cultural Systems ◽  
Development Plans ◽  
Theory Framework

“Elite Strategies, Local Networks” outlines analytic frameworks for studying the clash of development strategies and local social networks. Fennell starts by laying out definitions of ethnic groups and how individual action and group solidarity interact. Next, one considers how individual and group dynamics shape material culture and the built environment. Finally, Fennell describes the influences of development plans on regional scales and spatial models for understanding economic structures and commodity chains. The theory framework described here provides a powerful, flexible way to analyze a great variety of cultural systems.

scholarly journals Stacked Community Prediction: A Distributed Stacking-Based Community Extraction Methodology for Large Scale Social Networks

2021 ◽  
Vol 5 (1) ◽  
pp. 14
Author(s):  
Christos Makris ◽  
Georgios Pispirigos
Keyword(s):  
Social Networks ◽  
Graph Partitioning ◽  
Large Scale ◽  
Real Life ◽  
Information Networks ◽  
Digital Marketing ◽  
Partitioning Problems ◽  
Iterative Solutions ◽  
Community Extraction ◽  
Stability And Accuracy

Nowadays, due to the extensive use of information networks in a broad range of fields, e.g., bio-informatics, sociology, digital marketing, computer science, etc., graph theory applications have attracted significant scientific interest. Due to its apparent abstraction, community detection has become one of the most thoroughly studied graph partitioning problems. However, the existing algorithms principally propose iterative solutions of high polynomial order that repetitively require exhaustive analysis. These methods can undoubtedly be considered resource-wise overdemanding, unscalable, and inapplicable in big data graphs, such as today’s social networks. In this article, a novel, near-linear, and highly scalable community prediction methodology is introduced. Specifically, using a distributed, stacking-based model, which is built on plain network topology characteristics of bootstrap sampled subgraphs, the underlined community hierarchy of any given social network is efficiently extracted in spite of its size and density. The effectiveness of the proposed methodology has diligently been examined on numerous real-life social networks and proven superior to various similar approaches in terms of performance, stability, and accuracy.

scholarly journals The Role of Risk Preferences in Responses to Messaging About COVID-19 Vaccine Take-Up

2021 ◽  
pp. 194855062199962
Author(s):  
Jennifer S. Trueblood ◽  
Abigail B. Sussman ◽  
Daniel O’Leary
Keyword(s):  
Large Scale ◽  
Risk Preferences ◽  
Herd Immunity ◽  
Disease Spread ◽  
Short Term ◽  
Policy Makers ◽  
Health Crisis ◽  
Flu Vaccine ◽  
Nationally Representative

Development of an effective COVID-19 vaccine is widely considered as one of the best paths to ending the current health crisis. While the ability to distribute a vaccine in the short-term remains uncertain, the availability of a vaccine alone will not be sufficient to stop disease spread. Instead, policy makers will need to overcome the additional hurdle of rapid widespread adoption. In a large-scale nationally representative survey ( N = 34,200), the current work identifies monetary risk preferences as a correlate of take-up of an anticipated COVID-19 vaccine. A complementary experiment ( N = 1,003) leverages this insight to create effective messaging encouraging vaccine take-up. Individual differences in risk preferences moderate responses to messaging that provides benchmarks for vaccine efficacy (by comparing it to the flu vaccine), while messaging that describes pro-social benefits of vaccination (specifically herd immunity) speeds vaccine take-up irrespective of risk preferences. Findings suggest that policy makers should consider risk preferences when targeting vaccine-related communications.

scholarly journals Exploiting Proximity-Based Mobile Apps for Large-Scale Location Privacy Probing

2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Shuang Zhao ◽  
Xiapu Luo ◽  
Xiaobo Ma ◽  
Bo Bai ◽  
Yankang Zhao ◽  
...  
Keyword(s):  
Social Networks ◽  
Case Studies ◽  
Real World ◽  
Emission Reduction ◽  
Large Scale ◽  
Location Privacy ◽  
Mobile Apps ◽  
Physical World ◽  
Typical Type ◽  
Location Spoofing

Proximity-based apps have been changing the way people interact with each other in the physical world. To help people extend their social networks, proximity-based nearby-stranger (NS) apps that encourage people to make friends with nearby strangers have gained popularity recently. As another typical type of proximity-based apps, some ridesharing (RS) apps allowing drivers to search nearby passengers and get their ridesharing requests also become popular due to their contribution to economy and emission reduction. In this paper, we concentrate on the location privacy of proximity-based mobile apps. By analyzing the communication mechanism, we find that many apps of this type are vulnerable to large-scale location spoofing attack (LLSA). We accordingly propose three approaches to performing LLSA. To evaluate the threat of LLSA posed to proximity-based mobile apps, we perform real-world case studies against an NS app named Weibo and an RS app called Didi. The results show that our approaches can effectively and automatically collect a huge volume of users’ locations or travel records, thereby demonstrating the severity of LLSA. We apply the LLSA approaches against nine popular proximity-based apps with millions of installations to evaluate the defense strength. We finally suggest possible countermeasures for the proposed attacks.

Exploiting Social Networks for Large-Scale Human Behavior Modeling

2011 ◽  
Vol 10 (4) ◽  
pp. 45-53 ◽  
Author(s):  
Nicholas D. Lane ◽  
Ye Xu ◽  
Hong Lu ◽  
Andrew T. Campbell ◽  
Tanzeem Choudhury ◽  
...  
Keyword(s):  
Social Networks ◽  
Human Behavior ◽  
Large Scale ◽  
Behavior Modeling ◽  
Human Behavior Modeling

Pots on mats: mat-impressed salt-extraction pottery at Chalcolithic Provadia-Solnitsata, Bulgaria

Antiquity ◽  
2021 ◽  
pp. 1-16
Author(s):  
Mila Andonova ◽  
Vassil Nikolov
Keyword(s):  
Material Culture ◽  
Large Scale ◽  
Raw Materials ◽  
Salt Production ◽  
Production And Consumption ◽  
Late Chalcolithic ◽  
South Eastern Europe ◽  
Materials Used ◽  
The Relationship

Evidence for both basket weaving and salt production is often elusive in the prehistoric archaeological record. An assemblage of Middle–Late Chalcolithic pottery from Provadia-Solnitsata in Bulgaria provides insight into these two different technologies and the relationship between them. The authors analyse sherds from vessels used in large-scale salt production, the bases of which bear the impression of woven mats. This analysis reveals the possible raw materials used in mat weaving at Provadia-Solnitsata and allows interpretation of the role of these mats in salt production at the site. The results illustrate how it is possible to see the ‘invisible’ material culture of prehistoric south-eastern Europe and its importance for production and consumption.

Efficient Vector Partitioning Algorithms for Graph Clustering

2020 ◽  
Vol 1 (2) ◽  
pp. 101-123
Author(s):  
Hiroaki Shiokawa ◽  
Yasunori Futamura
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Ground Truth ◽  
Graph Clustering ◽  
Mining Communities ◽  
Fine Grained ◽  
Efficient Vector ◽  
Public Datasets ◽  
Many Core

This paper addressed the problem of finding clusters included in graph-structured data such as Web graphs, social networks, and others. Graph clustering is one of the fundamental techniques for understanding structures present in the complex graphs such as Web pages, social networks, and others. In the Web and data mining communities, the modularity-based graph clustering algorithm is successfully used in many applications. However, it is difficult for the modularity-based methods to find fine-grained clusters hidden in large-scale graphs; the methods fail to reproduce the ground truth. In this paper, we present a novel modularity-based algorithm, \textit{CAV}, that shows better clustering results than the traditional algorithm. The proposed algorithm employs a cohesiveness-aware vector partitioning into the graph spectral analysis to improve the clustering accuracy. Additionally, this paper also presents a novel efficient algorithm \textit{P-CAV} for further improving the clustering speed of CAV; P-CAV is an extension of CAV that utilizes the thread-based parallelization on a many-core CPU. Our extensive experiments on synthetic and public datasets demonstrate the performance superiority of our approaches over the state-of-the-art approaches.

Assessing Triangulation Across Methodologies, Methods, and Stakeholder Groups: The Joys, Woes, and Politics of Interpreting Convergent and Divergent Data

2018 ◽  
Vol 41 (1) ◽  
pp. 125-144 ◽  
Author(s):  
Rebecca Campbell ◽  
Rachael Goodman-Williams ◽  
Hannah Feeney ◽  
Giannina Fehler-Cabral
Keyword(s):  
Large Scale ◽  
Contextual Information ◽  
Policy Makers ◽  
Specific Data ◽  
Qualitative And Quantitative ◽  
Stakeholder Groups ◽  
Data Points ◽  
Evaluation Project ◽  
Collection Methods

The purpose of this study was to develop triangulation coding methods for a large-scale action research and evaluation project and to examine how practitioners and policy makers interpreted both convergent and divergent data. We created a color-coded system that evaluated the extent of triangulation across methodologies (qualitative and quantitative), data collection methods (observations, interviews, and archival records), and stakeholder groups (five distinct disciplines/organizations). Triangulation was assessed for both specific data points (e.g., a piece of historical/contextual information or qualitative theme) and substantive findings that emanated from further analysis of those data points (e.g., a statistical model or a mechanistic qualitative assertion that links themes). We present five case study examples that explore the complexities of interpreting triangulation data and determining whether data are deemed credible and actionable if not convergent.

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