Laborers and Smallholders in Costa Rica's Mining Communities, 1900-1940

Keyword(s):  
2014 ◽  
Vol 52 (4) ◽  
pp. 541-570 ◽  
Author(s):  
Samuel J. Spiegel

ABSTRACTAlthough conflict in Zimbabwe's diamond mining sector has recently received much international scrutiny, very little research has examined conflict in Zimbabwe's gold mining sector. This article analyses how a nationwide crackdown calledOperation Chikorokoza Chapera(‘No More Illegal Mining’) affected – and ‘disciplined’ – livelihoods in profound ways in both licensed and unlicensed gold mining regions. Drawing on interviews conducted between 2006 and 2013 with artisanal miners in the Insiza, Umzingwani and Kadoma areas as well as miners who crossed the border to Mozambique, the study reveals how a highly politicised crackdown led to uneven consequences. The analysis highlights both structural and physical violence, with more than 25,000 miners and traders arrested between 2006 and 2009 and more than 9,000 still imprisoned in 2013. Situating the crackdown within evolving political and economic interests, the study contributes to an understanding of how simplified discourses on ‘eradicating illegal mining’ mislead and mask power dynamics, while policing activities transform patterns of resource control. The study also emphasises that conceptualisations of the crackdown's legacy should carefully consider the agency of artisanal miners' associations, which, in some cases, have been actively seeking to resist coercive policies and rebuild livelihoods in the aftermath ofOperation Chikorokoza Chapera.


2020 ◽  
Vol 1 (2) ◽  
pp. 101-123
Author(s):  
Hiroaki Shiokawa ◽  
Yasunori Futamura

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.


Author(s):  
José R. Velásquez ◽  
Michelle Schwartz ◽  
Laura M. Phipps ◽  
Oscar Jaime Restrepo-Baena ◽  
Juan Lucena ◽  
...  

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