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published_or_final_version / Business Administration / Master / Master of Business Administration

published_or_final_version / abstract / Business / Master / Master of Philosophy

published_or_final_version / Education / Doctoral / Doctor of Philosophy

While most scholars agree that the development of increasingly sophisticated computer-based technologies over the past thirty years and their ubiquitous use in work settings are important technological transformations, it is still question whether they constitute large-scale and meaningful social transformations. In this dissertation, it is argued that transformations cannot be understood by studying technologies in isolated and circumscribed analyses, rather they must be understood in the historical and socio-political context of their development and use. Several important questions are being asked: Will social relations in work settings be transformed so that they are more collaborative and less hierarchical, as many proponents of new group support systems predict? Will workers in computer-using organizations share equally in the production and control of skills and knowledge? Or will the use of new technologies reinforce and reproduce the current distribution of power, authority and knowledge in organizations? In order to answer these questions, a meta-analytic framework is developed. It comprises a continuum from micro- to macro-social interaction contexts, including six key fields of action surrounding the use of new technologies: (1) design; (2) use; (3) infrastructure of support; (4) work group governance; (5) organizational contexts; and (6) organizational fields. Four field studies are conducted with in vivo, ongoing organizational work groups using three new computer-based information technologies. There is little indication that hierarchical forms of work group governance are being restructured along the lines of more flexible and collaborative forms of work organization. There is, however, some evidence for power shifts among relatively disenfranchised high status participants in ongoing project teams. In addition, distinctive cultures emerged in ongoing groups that used group collaboration systems. In the desktop computing and desktop group support system work groups, skills and knowledge about their own computing environment were differentially distributed, so that lower status workers were less knowledgeable. Thus, the routine use of new technologies is most likely to reinforce the current distribution of authority and power in organizations.

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While technology has widely been formulated as antithetical to nature, there has been an increased adoption of digital set-ups to promote and enact environmental conservation. This thesis thus examined a range of digital technologies more commonly used for nature-related activities (for example, mobile applications for crowdsourcing data, satellite tracking and mapping facilities, and visual imaging equipment such as cameras and sonar devices) with two objectives. First, at an applied level, the research sought to locate the new set-ups being used, and to unfold the technical, practical and relational issues emerging from this use. Second, at a more abstract level, the research aimed to better understand the sociological implications of deploying these technologies, in terms of the definitions of 'nature' being 'produced' and how the devices might be (re)shaping human-nature relationships. Four areas were studied: wildlife monitoring and recording, public engagement efforts by conservation organisations, conflict management, and digital art production. These contexts form the data chapters of this thesis, and the findings result from an inter-disciplinary qualitative social scientific research enquiry, framed by a constructionist perspective. With regard to the first aim, this research found that the technologies used by organisations and practitioners had the capacity to increase public participation as well as the quantity and quality of nature-related data and information, and could contribute to the formulation of environmental conservation strategies. However, these capacities did not come without issues such as the relegation of public participants to passive roles and struggles over legitimacy in terms of production and interpretation of data wrought from new devices. In relation to the second aim, this research found that digital technological set-ups (re)configured the ways in which wildlife in particular was seen and understood, and revealed both enmeshment and persistent binaries along the emotion/cognition and nature/culture axes. These findings highlight the role of emotions in conservation, and point to increasing complexities in how humans define and relate to nature.

This dissertation examines the transmissions of technology and changes in the culture of statecraft by tracing the itinerary of tin from ore in mines to everyday objects. From the eighteenth century, with the expansion of the Qing empire and global trade, miners migrated from the east coast of China to the southwest frontiers of the Qing empire (1644-1912) and into Southeast Asia, bringing their mining technology with them. The tin from Southeast Asia, in return, inspired Chinese pewter artisans to invent new styles and techniques of metalworking. Furthermore, the knowledge of mining, metalworking, and trade was transferred from miners, artisans, and merchants into the knowledge system of scholar-officials, gradually changing the culture of statecraft in the Qing dynasty. This dissertation explores how imperial expansion and the intensive material exchange brought by global trade affected knowledge production and transmission, gradually changing the culture of statecraft in China. In the Qing dynasty, people used tin, the component of two common alloys, pewter and bronze, to produce objects of daily use as well as copper coins. Thus, tin was not only important to people’s everyday lives, but also to the policy-making of the Qing state. In this way, tin offers an exceptional opportunity to investigate artisans and intellectuals’ approach to technology, while it also provides a vantage point from which to examine how Qing bureaucrats managed the world, a world of human and non-human resources. My dissertation stands at the intersection of the history of science and technology, art history, intellectual history, and the history of global trade. It broadens the scope of the history of science in China by demonstrating how artisans’ practice was crucial to the production of mining treatises. It contributes to the study of science, technology, and society by showing that the transmission of and innovations in technology should be situated in the context of social, cultural, trade, and ecological networks. Finally, I argue that mid-Qing scholars’ efforts to collect practical knowledge changed the culture of governance from Confucian moral didacticism to technocratic epistemology. Qing bureaucrats, Manchu and Han alike, utilized practical knowledge from artisans and merchants in their policy-making process. By emphasizing the entanglement of technology and statecraft, my project contributes to intellectual history and enhances our understanding of the logic of bureaucracy of the Qing empire. My dissertation consists of five chapters. Each chapter uses different methodologies and covers different geographical regions. Chapter One engages with the history of science by demonstrating how scholars translated and codified miners’ vernacular knowledge of mining into mining treatises. Chapter Two examines the semi-autonomous mining community in Yunnan to illustrate that the social organization of miners, which I define as the “social technology” of mining, contributed to the formation of the capital- and labor- intensive mining industry. Chapter Three moves to the island of Bangka (in present-day Indonesia) and focuses on the transmission of mining technology from China to Southeast Asia. Through comparison, I show that the miners in Yunnan and Bangka formed similar (semi-)autonomous social organizations. I argue that it was this social technology that enabled the transmission of Chinese mining technology across geographical regions and laid the foundation for the Chinese dominance of the mining industry in Bangka. The cases of Chinese mining technology in Yunnan and Bangka challenge the modern understanding of technology by showing that technology was not just about tools and machines. Before the 1850s, both Qing bureaucrats and European colonizers considered the social organization of mining to be critical to technological progress. Chapter Four moves back to China to study the formation of Guangdong style pewter. Utilizing visual and material sources, I examine how the introduction of tin from Southeast Asia led to innovations in metallurgy, and how European silver and porcelain inspired stylistic changes. I argue that technology and innovations should be understood in the context of social, economic, material and ecological networks. The final chapter moves to Beijing and Jiangnan area to engage with the institutional history of the Qing empire. Through a case study of monetary reform undertaken in 1740, this chapter reveals that Qing bureaucrats acquired and applied practical expertise to their administrative work. Through their close interactions with artisans and merchants, Qing bureaucrats developed a distinctive vision of statecraft (jingshi). Before the late nineteenth century, the sovereignty of the Qing state was not exercised in the extraction and monopoly over natural resources. Instead, the Qing state relied on the market to acquire most of the natural resources they needed. By focusing on tin, this dissertation shows that the Qing state exercised its political power through material production and paid more attention to the management of skilled labor, capital, and the proper allocation of human and non-human resources.

Developments in advanced technologies, such as DNA microarrays, have generated tremendous amounts of data available to researchers in the field of bioinformatics. These state-of-the-art technologies present not only unprecedented opportunities to study biological phenomena of interest, but significant challenges in terms of processing the data. Furthermore, these datasets inherently exhibit a number of challenging characteristics, such as class imbalance, high dimensionality, small dataset size, noisy data, and complexity of data in terms of hard to distinguish decision boundaries between classes within the data. In recognition of the aforementioned challenges, this dissertation utilizes a variety of machine-learning and data-mining techniques, such as ensemble classification algorithms in conjunction with data sampling and feature selection techniques to alleviate these problems, while improving the classification results of models built on these datasets. However, in building classification models researchers and practitioners encounter the challenge that there is not a single classifier that performs relatively well in all cases. Thus, numerous classification approaches, such as ensemble learning methods, have been developed to address this problem successfully in a majority of circumstances. Ensemble learning is a promising technique that generates multiple classification models and then combines their decisions into a single final result. Ensemble learning often performs better than single-base classifiers in performing classification tasks. This dissertation conducts thorough empirical research by implementing a series of case studies to evaluate how ensemble learning techniques can be utilized to enhance overall classification performance, as well as improve the generalization ability of ensemble models. This dissertation investigates ensemble learning techniques of the boosting, bagging, and random forest algorithms, and proposes a number of modifications to the existing ensemble techniques in order to improve further the classification results. This dissertation examines the effectiveness of ensemble learning techniques on accounting for challenging characteristics of class imbalance and difficult-to-learn class decision boundaries. Next, it looks into ensemble methods that are relatively tolerant to class noise, and not only can account for the problem of class noise, but improves classification performance. This dissertation also examines the joint effects of data sampling along with ensemble techniques on whether sampling techniques can further improve classification performance of built ensemble models. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection