Diamond mining, rice farming, and a ‘Maggi cube’: a viable survivial strategy in rural Liberia?

Since the conclusion of its 14-year civil war in 2003, Liberia has struggled economically. Jobs are in short supply and operational infrastructural services, such as electricity and running water, are virtually nonexistent. The situation has proved especially challenging for the scores of people who fled the country in the 1990s to escape the violence and who have since returned to re-enter their lives. With few economic prospects on hand, many have elected to enter the artisanal diamond mining sector, which has earned notoriety for perpetuating the country's civil war. This article critically reflects on the fate of these Liberians, many of whom, because of a lack of government support, finances, manpower and technological resources, have forged deals with hired labourers to work artisanal diamond fields. Specifically, in exchange for meals containing locally grown rice and a Maggi (soup) cube, hired hands mine diamondiferous territories, splitting the revenues accrued from the sales of recovered stones amongst themselves and the individual ‘claimholder’ who hired them. Although this cycle—referred to here as ‘diamond mining, rice farming and a Maggi cube’—helps to buffer against poverty, few of the parties involved will ever progress beyond a subsistence level

Structural adjustment and subsistence industry: artisanal gold mining in Ghana

Since the implementation of Ghana's national Structural Adjustment Programme (SAP), policies associated with the programme have been criticized for perpetuating poverty within the country's subsistence economy. This article brings new evidence to bear on the contention that the SAP has both fuelled the uncontrolled growth of informal, poverty-driven artisanal gold mining and further marginalized its impoverished participants. Throughout the adjustment period, it has been a central goal of the government to promote the expansion of large-scale gold mining through foreign investment. Confronted with the challenge of resuscitating a deteriorating gold mining industry, the government introduced a number of tax breaks and policies in an effort to create an attractive investment climate for foreign multinational mining companies. The rapid rise in exploration and excavation activities that has since taken place has displaced thousands of previously-undisturbed subsistence artisanal gold miners. This, along with a laissez faire land concession allocation procedure, has exacerbated conflicts between mining parties. Despite legalizing small-scale mining in 1989, the Ghanaian government continues to implement procedurally complex and bureaucratically unwieldy regulations and policies for artisanal operators which have the effect of favouring the interests of established large-scale miners.

PDM 2006: Proceedings of the International Workshop on Data Mining

Recently major processor manufacturers have announced a dramatic shift in their paradigm to increase computing power over the coming years. Instead of focusing on faster clock speeds and more powerful single core CPUs, the trend clearly goes towards multi core systems. This will also result in a paradigm shift for the development of algorithms for computationally expensive tasks, such as data mining applications. Obviously, work on parallel algorithms is not new per se but concentrated efforts in the many application domains are still missing. Multi-core systems, but also clusters of workstations and even large-scale distributed computing infrastructures provide new opportunities and pose new challenges for the design of parallel and distributed algorithms. Since data mining and machine learning systems rely on high performance computing systems, research on the corresponding algorithms must be on the forefront of parallel algorithm research in order to keep pushing data mining and machine learning applications to be more powerful and, especially for the former, interactive. To bring together researchers and practitioners working in this exciting field, a workshop on parallel data mining was organized as part of PKDD/ECML 2006 (Berlin, Germany). The six contributions selected for the program describe various aspects of data mining and machine learning approaches featuring low to high degrees of parallelism: The first contribution focuses the classic problem of distributed association rule mining and focuses on communication efficiency to improve the state of the art. After this a parallelization technique for speeding up decision tree construction by means of thread-level parallelism for shared memory systems is presented. The next paper discusses the design of a parallel approach for dis- tributed memory systems of the frequent subgraphs mining problem. This approach is based on a hierarchical communication topology to solve issues related to multi-domain computational envi- ronments. The forth paper describes the combined use and the customization of software packages to facilitate a top down parallelism in the tuning of Support Vector Machines (SVM) and the next contribution presents an interesting idea concerning parallel training of Conditional Random Fields (CRFs) and motivates their use in labeling sequential data. The last contribution finally focuses on very efficient feature selection. It describes a parallel algorithm for feature selection from random subsets. Selecting the papers included in this volume would not have been possible without the help of an international Program Committee that has provided detailed reviews for each paper. We would like to also thank Matthew Otey who helped with publicity for the workshop.

Family hardship and cultural values: child labor in Malian artisanal gold mining communities

This paper contributes to the debate on child labor in small-scale mining communities, focusing specifically on the situation in sub-Saharan Africa. It argues that the child labor now widespread in many of the region’s small-scale mining communities is a product of a combination of cultural issues, household-level poverty and rural livelihood diversification. Experiences from Komana West, a subsistence gold panning area in Southern Mali, are drawn upon to make this case. The findings suggest that the sector’s child labor “problem” is far more nuanced than international organizations and policymakers have diagnosed.

Poverty traps in small-scale mining communities: the case of sub-Saharan Africa

This paper critically reflects on why, in many rural stretches of sub-Saharan Africa, scores of people engage in artisanal and small-scale mining (ASM) activity – low-tech, labour intensive mineral extraction – for lengthy periods of time. It argues that a large share of the region’s ASM operators have mounting debts which prevent them from pursuing alternative, less arduous, employment. The paper concludes with an analysis of findings from research carried out by the author in Talensi-Nabdam District, Northern Ghana, which captures the essence of the poverty trap now plaguing so many ASM communities in sub-Saharan Africa.

‘Everyone now is concentrating on the mining’: drivers and implications of rural economic transition in the Eastern Region of Ghana

Artisanal and small-scale mining (ASM) is replacing smallholder farming as the principal income source in parts of rural Ghana. Structural adjustment policies have removed support for the country’s smallholders, devalued their produce substantially and stiffened competition with large-scale counterparts. Over one million people nationwide are now engaged in ASM. Findings from qualitative research in Ghana’s Eastern Region are drawn upon to improve understanding of the factors driving this pattern of rural livelihood diversification. The ASM sector and farming are shown to be complementary, contrary to common depictions in policy and academic literature.

Distributed classification for pocket data mining

Distributed and collaborative data stream mining in a mobile computing environment is referred to as Pocket Data Mining PDM. Large amounts of available data streams to which smart phones can subscribe to or sense, coupled with the increasing computational power of handheld devices motivates the development of PDM as a decision making system. This emerging area of study has shown to be feasible in an earlier study using technological enablers of mobile software agents and stream mining techniques [1]. A typical PDM process would start by having mobile agents roam the network to discover relevant data streams and resources. Then other (mobile) agents encapsulating stream mining techniques visit the relevant nodes in the network in order to build evolving data mining models. Finally, a third type of mobile agents roam the network consulting the mining agents for a final collaborative decision, when required by one or more users. In this paper, we propose the use of distributed Hoeffding trees and Naive Bayes classifers in the PDM framework over vertically partitioned data streams. Mobile policing, health monitoring and stock market analysis are among the possible applications of PDM. An extensive experimental study is reported showing the effectiveness of the collaborative data mining with the two classifers.

Artisanal gold mining at the margins of mineral resource governance : a case from Tanzania

This article discusses the character of mineral resource governance at the margins of the state in Tanzania and the way artisanal gold miners are incorporated into mineral sector transformation. The landscape of mineral resource exploitation has changed dramatically over the past 20 years: processes of economic liberalisation have heralded massive foreign investment in large-scale gold mining, while also stimulating artisanal activities. Against this background, the article shows how artisanal gold miners are affected by contradictory processes: some have become integrated with state institutions and legal processes, while others, the large majority, are either further excluded or incorporated in ways that exacerbate insecurity and exploitation, underpinned by socio-economic inequalities. These processes are compounded by the actions of large-scale and medium-scale gold mining companies and by poor local governance. It is open to debate whether this will bring improved integration and welfare for artisanal mining communities or new forms of exclusion, although evidence suggests the latter.

Scaling up data mining techniques to large datasets using parallel and distributed processing

Advances in hardware and software technology enable us to collect, store and distribute large quantities of data on a very large scale. Automatically discovering and extracting hidden knowledge in the form of patterns from these large data volumes is known as data mining. Data mining technology is not only a part of business intelligence, but is also used in many other application areas such as research, marketing and financial analytics. For example medical scientists can use patterns extracted from historic patient data in order to determine if a new patient is likely to respond positively to a particular treatment or not; marketing analysts can use extracted patterns from customer data for future advertisement campaigns; finance experts have an interest in patterns that forecast the development of certain stock market shares for investment recommendations. However, extracting knowledge in the form of patterns from massive data volumes imposes a number of computational challenges in terms of processing time, memory, bandwidth and power consumption. These challenges have led to the development of parallel and distributed data analysis approaches and the utilisation of Grid and Cloud computing. This chapter gives an overview of parallel and distributed computing approaches and how they can be used to scale up data mining to large datasets.

‘Cursed’ communities? Corporate Social Responsibility (CSR), company towns and the mining industry in Namibia

This article examines Corporate Social Responsibility (CSR) and mining community development, sustainability and viability. These issues are considered focussing on current and former company-owned mining towns in Namibia. Historically company towns have been a feature of mining activity in Namibia. However, the fate of such towns upon mine closure has been and remains controversial. Declining former mining communities and even ghost mining towns can be found across the country. This article draws upon research undertaken in Namibia and considers these issues with reference to three case study communities. This article examines the complexities which surround decision-making about these communities, and the challenges faced in efforts to encourage their sustainability after mining. In this article, mine company engagements through CSR with the development, sustainability and viability of such communities are also critically discussed. The role, responsibilities, and actions of the state in relation to these communities are furthermore reflected upon. Finally, ways forward for these communities are considered.

The creativity of action: Property, Kin and the social in African artisanal mining

Analyses of neo-liberal change in African mining tend to frame discussion through the lens of an overarching structural perspective. Far less attention has been paid to the way change is enacted within social relations in mining communities. To this end, our chapter considers how development in the Tanzanian mineral sector transforms people’s relationships and stimulates new iterations of power and agency within local trajectories of development, focusing on the case of artisanal gold mining in Mgusu village in Geita region, Tanzania. The aim is to trace how neo-liberal change configures market rationality and property relations in ways that can fundamentally alter social relationships within the local community, occupational groups and families, raising both opportunities for wealth accumulation and the potential to entrench poverty. The creative action involved in these processes generates new associational ties and repertoires of practice, as miners’ respond to change and the need to protect their livelihoods.