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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Technical Dimensions of Programming Systems
Programming requires much more than just writing code in a programming language. It is usually done in the context of a stateful environment, by interacting with a system through a graphical user interface. Yet, this wide space of possibilities lacks a common structure for navigation. Work on programming systems fails to form a coherent body of research, making it hard to improve on past work and advance the state of the art.
In computer science, much has been said and done to allow comparison of programming languages, yet no similar theory exists for programming systems; we believe that programming systems deserve a theory too.
We present a framework of technical dimensions which capture the underlying characteristics of programming systems and provide a means for conceptualizing and comparing them.
We identify technical dimensions by examining past influential programming systems and reviewing their design principles, technical capabilities, and styles of user interaction. Technical dimensions capture characteristics that may be studied, compared and advanced independently. This makes it possible to talk about programming systems in a way that can be shared and constructively debated rather than relying solely on personal impressions.
Our framework is derived using a qualitative analysis of past programming systems. We outline two concrete ways of using our framework. First, we show how it can analyze a recently developed novel programming system. Then, we use it to identify an interesting unexplored point in the design space of programming systems.
Much research effort focuses on building programming systems that are easier to use, accessible to non-experts, moldable and/or powerful, but such efforts are disconnected. They are informal, guided by the personal vision of their authors and thus are only evaluable and comparable on the basis of individual experience using them. By providing foundations for more systematic research, we can help programming systems researchers to stand, at last, on the shoulders of giants
Corporate Social Responsibility: the institutionalization of ESG
Understanding the impact of Corporate Social Responsibility (CSR) on firm performance as it relates to industries reliant on technological innovation is a complex and perpetually evolving challenge. To thoroughly investigate this topic, this dissertation will adopt an economics-based structure to address three primary hypotheses. This structure allows for each hypothesis to essentially be a standalone empirical paper, unified by an overall analysis of the nature of impact that ESG has on firm performance. The first hypothesis explores the evolution of CSR to the modern quantified iteration of ESG has led to the institutionalization and standardization of the CSR concept. The second hypothesis fills gaps in existing literature testing the relationship between firm performance and ESG by finding that the relationship is significantly positive in long-term, strategic metrics (ROA and ROIC) and that there is no correlation in short-term metrics (ROE and ROS). Finally, the third hypothesis states that if a firm has a long-term strategic ESG plan, as proxied by the publication of CSR reports, then it is more resilience to damage from controversies. This is supported by the finding that pro-ESG firms consistently fared better than their counterparts in both financial and ESG performance, even in the event of a controversy. However, firms with consistent reporting are also held to a higher standard than their nonreporting peers, suggesting a higher risk and higher reward dynamic. These findings support the theory of good management, in that long-term strategic planning is both immediately economically beneficial and serves as a means of risk management and social impact mitigation. Overall, this contributes to the literature by fillings gaps in the nature of impact that ESG has on firm performance, particularly from a management perspective
Scientists’ attitudes about citizen science at Long-Term Ecological Research (LTER) sites
A profound transformation, in recent decades, is promoting shifts in the ways ecological science is produced and shared; as such, ecologists are increasingly encouraged to engage in dialogues with multiple stakeholders and in transdisciplinary research. Among the different forms of public engagement, citizen science (CS) has significant potential to support science-society interactions with mutual benefits. While many studies have focused on the experience and motivations of CS volunteers, scarce literature investigating the perspectives of researchers is available. The main purpose of this paper is to better understand scientists’ attitudes about CS in the context of its potential to support outcomes that extent beyond more traditional ones focused on promoting science knowledge and interest. We surveyed the scientific community belonging to the International Long-Term Ecological Research (ILTER) network because ILTER is of interest to multiple stakeholders and occurs over long time scales. Via an online questionnaire, we asked ILTER scientists about their willingness to participate in different types of public engagement, their reasons for participating in CS, the associated barriers, and any impacts of these efforts on them. Our findings show that many ILTER scientists are open to participating in CS for a wide range of reasons; the dominant ones involve deeper public engagement and collaboration. The barriers of greatest concern of these respondents were the lack of institutional support to start and run a CS project and the difficulty of establishing long-term stable relationships with the public. They reported impacts of CS activities on how they pursue their work and acknowledged the benefit of opportunities to learn from the public. The emerging picture from this research is of a community willing and actively involved in many CS projects for both traditional reasons, such as data gathering and public education, and expanded reasons that activate a real two-way cooperation with the public. In the ILTER community, CS may thus become an opportunity to promote and develop partnerships with citizens, helping to advance the science-society interface and to rediscover and enhance the human and social dimension of the scientific work
A Phenomenological Study of How Active Engagement in Black Greek Letter Sororities Influences Christian Members\u27 Spiritual Growth
This phenomenological study explored how being part of a Black Greek Letter. Organization (BGLO) sorority impacts the spiritual growth of its Christian members. One of the issues explored was the influence relationships within these sororities have on members striving to be like Christ. There is a dichotomy of perspectives regarding Black Greek Letter Organizations (BGLOs). They have a significant role in the Black community as organizations that foster leadership, philanthropy, and sisterhood and promote education. They are admired on and off college campuses and in the broader community in graduate chapters. The objective of phenomenology is to describe phenomena of spiritual growth among Christian sorority members from the life experiences of those who live them; that premise guided the interviews conducted for this study. The results found that active engagement in a BGLO sorority positively impacts its members\u27 spiritual growth. From the emotional stories of sisterhood, service, and devotion to prayer, their experiences evidenced strengthened walks of faith. This study contrasts the Anti-BGLO narrative as a testament to these organizations\u27 legacy and practices deeply grounded in the church
Establishing a Data Science for Good Ecosystem: The Case of ATLytiCS
Data science for social good (DSSG) initiatives have been championed as worthy mechanisms for transformative change and social impact. However, researchers have not fully explored the systems by which actors coordinate, access data, determine goals and communicate opportunities for change. We contribute to the information systems ecosystems and the nonprofit volunteering literatures by exploring the ways in which data science volunteers leverage their talents to address social impact goals. We use Atlanta Analytics for Community Service (ATLytiCS), an organization that aids nonprofits and government agencies, as a case study. ATLytiCS represents a rare example of a nonprofit organization (NPO) managed and run by highly-skilled volunteer data scientists within a regionally networked system of actors and institutions. Based on findings from this case, we build a DSSG ecosystem framework to describe and distinguish DSSG ecosystems from related data and entrepreneurial ecosystems
Big Tech and research funding: A bibliometric approach
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsTechnology companies have radically transformed our daily life in the recent years with help of the wide usage of internet. While transforming our lives, these companies also have grown up even bigger in the recent times and have become more powerful not only financially, but also in terms of computing power and data. Although there have been lots of research done on the influence of large digital economy players (Big Tech) in different fields, the academic influence of these companies is little understood. By drawing on 130,000 academic papers for which there is evidence of support by the Big Tech, the present work applies bibliometric approaches (on the metadata) and text mining techniques (on the contents) to shed a light on the outcomes of this relationship. In particular, we take into consideration research funding (direct strategies) and conference sponsorships (indirect strategies) to empirically explore this relatively unexplored side of Big Tech’s influence in contemporary society. While developing the analysis a key limitation was the scarcity of prior work exploring the connections between digital platforms and the scientific enterprise. There are several results that come to light from such a perspective, one of these findings is that among the research supported by Big Tech companies, there is big gap between the number of outcomes with the content about the technical perspectives (like machine learning or artificial intelligence) than the content about reflexive (say ethical or environmental) dimensions of innovation, ladder being very small. These findings may stimulate further inquiries into identifying the possible risks, if any, are generated from the direct and indirect financial support by corporate informational giants to academia. The causes and consequences of this non-market activity by companies with big market power may require further attention and research in this field
Exploring Potential Domains of Agroecological Transformation in the United States
There is now substantial evidence that agroecology constitutes a necessary pathway towards socially just and ecologically resilient agrifood systems. In the United States, however, agroecology remains relegated to the margins of research and policy spaces. This dissertation explores three potential domains of agroecological transformation in the US. Domains of transformation are sites of contestation in which agroecology interfaces with the industrial agrifood system; these material and conceptual spaces may point to important pathways for scaling agroecology. To explore this concept, I examine formal agroecology education (Chapter 1), extension services and statewide discourses around soil health (Chapter 2), and models of farmland access not based on private property (Chapter 3). While these constitute three distinct topics, I seek to demonstrate that they are linked by similar forces that enable and constrain the extent to which these domains can be sites of agroecological transformation.
First, I use case study methodology to explore the evolution of an advanced undergraduate agroecology course at the University of Vermont. I examine how course content and pedagogy align with a transformative framing of agroecology as inherently transdisciplinary, participatory, action-oriented, and political. I find that student-centered pedagogies and experiential education on farms successfully promote transformative learning whereby students shift their understanding of agrifood systems and their role(s) within them. In my second chapter, I zoom out to consider soil health discourses amongst farmers and extension professionals in Vermont. Using co-created mental models and participatory analysis, I find that a singular notion of soil health based on biological, chemical, and physical properties fails to capture the diverse ways in which farmers and extension professionals understand soil health. I advocate for a principles-based approach to soil health that includes social factors and may provide a valuable heuristic for mobilizing knowledge towards agroecology transition pathways. My third chapter, conducted in collaboration with the national non-profit organization Agrarian Trust, considers equitable farmland access. Through semi-structured interviews with 13 farmers and growers across the US, I explore both farmer motivations for engaging with alternative land access models (ALAMs) and the potential role(s) these models may play within broader transformation processes. I argue that ALAMs constitute material and conceptual ‘third spaces’ within which the private property regime is challenged and new identities and language around land ownership can emerge; as such, ALAMs may facilitate a (re)imagining of land-based social-ecological relationships.
I conclude the dissertation by identifying conceptual and practical linkages across the domains explored in Chapters 1-3. I pay particular attention to processes that challenge neoliberal logics, enact plural ways of knowing, and prefigure just futures. In considering these concepts, I apply an expansive notion of pedagogy to explore how processes of teaching and (un)learning can contribute to cultivating foundational capacities for transition processes
Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review
In this paper, a critical bibliometric analysis study is conducted, coupled
with an extensive literature survey on recent developments and associated
applications in machine learning research with a perspective on Africa. The
presented bibliometric analysis study consists of 2761 machine learning-related
documents, of which 98% were articles with at least 482 citations published in
903 journals during the past 30 years. Furthermore, the collated documents were
retrieved from the Science Citation Index EXPANDED, comprising research
publications from 54 African countries between 1993 and 2021. The bibliometric
study shows the visualization of the current landscape and future trends in
machine learning research and its application to facilitate future
collaborative research and knowledge exchange among authors from different
research institutions scattered across the African continent
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