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Does AI at Work Increase Stress? Text Mining Social Media About Human–AI Team Processes and AI Control
No full textWith rising use of artificial intelligence (AI) in organizations, alongside increasing mental health issues, we seek to understand how AI use affects human stress. Drawing on the automation–augmentation perspective, we propose that AI control over decision-making thwarts human autonomy and thus contributes to stress. Drawing on models of teamwork and augmentation, we expect that human–AI team processes (i.e., transition, action, and interpersonal processes) help people meet their goals and reduce stress. Finally, we argue that human–AI team processes provide an important social resource, which buffers the stress-enhancing role of AI control. To test our hypotheses, we analyzed over 2700 tweets. Using a trained large language model, validated against human ratings, we indexed key measures. Results confirm that high AI control was associated with increased stress, whereas human–AI team processes were associated with decreased stress. In support of the moderation hypothesis, two human–AI team processes (action and interpersonal) helped further reduce the stress-enhancing effect of AI control. We discuss implications for work design theory and the importance of regulating levels of AI control to protect workers' mental health
Climate change, social environment, health, and urban inequality: Developing a novel adaptive evaluation framework
No full textWith the increasing trend of population concentration and more frequent disaster events in cities, understanding the dynamics of urban inequalities of cities has become crucial for enabling faster and more efficient citizen responses with reduced risks. However, existing models often fail to effectively capture the complex and multifaceted nature of urban inequalities due to their inability to represent the weights and dynamic changes of the evaluation framework. To address this limitation, we propose a novel adaptive framework based on Bidirectional Encoder Representations from Transformers (BERT) that leverages a literature-driven meta-analysis to comprehensively assess the spatial dynamics of the Urban Inequality Index (UII). The framework involves a structured three-step process. First, we gather and screen 35, 819 in total scholarly literature within defined temporal scopes to acquire UII related to economic, educational, infrastructural, environmental, social, and health dimensions to ensure a broad and diverse dataset. Second, we employ BERT and a weights calculation algorithm to identify and weigh potential indicators, providing a contextualized understanding that surpasses conventional analytic techniques. Finally, we amalgamate and process data from assorted sources to formulate a panel dataset, facilitating the computation of the UII, followed by spatial analysis and visualization to expose underlying patterns and trends. We applied this framework to reveal significant spatial dynamics and trends in UII for the periods 2000–2010 and 2010–2020, using Shanghai as a case study. Our findings emphasize the increasing importance of environmental indicators in UII evaluation, reflecting broader global trends toward sustainability and resilience. These results underscore the need for continuous monitoring and adaptive policies to mitigate urban inequality and promote more equitable urban development effectively and also highlight the evolution of the urban inequality assessment framework from 2010 to 2020 illustrating a strategic pivot towards addressing environmental sustainability and resilience against climate change and integrating green infrastructure into urban planning. This study provides a valuable tool for urban planners and policymakers to assess and address urban inequality, offering a replicable and adaptable framework to advance sustainable urban planning practices and support sustainable urban development globally
Evaluation of Barrier Materials for Leaching Solution Control in In-situ Recovery Processing
No full textThis research evaluated barrier materials for confining In-situ-recovery (ISR) operations within leaching boundaries and mitigating potential contaminant escape. Tailings-GGBFS binary mixtures were alkali-activated to form geopolymers. These composites exhibited high compressive strength (99 MPa) and low permeability (< 10?¹8 m2). The geopolymers showed significant stability in simulated downhole environments up to a depth of 2000 m, and incorporating mineral tailings improved their acid resistance. This performance meets and exceeds the functional requirements of ISR operations
Enhancing the Stability and Performance of Granular Sludge Reactors: Mathematical Modelling and Experimental Validation for Industrial Applications
Get PDFThis study investigates optimal operational conditions for stable granule formation in granular sludge reactors through data-driven modeling and experimental validation. Cluster analysis categorized reactors by settling velocity, aiding model development. Key factors influencing granulation were identified, including aeration time and organic loading rate. Experimental validation using three reactors confirmed model predictions. Results highlight developed two models successfully used in application of aerobic granular reactor operations. The findings offer practical guidance for industrial applications to improve wastewater treatment efficiency and reactor performance
Circular Economy: The key link between learning orientations and competitive advantage in Chilean small and medium-sized enterprises
No full textA survey of Australian dairy farmers’ attitudes to their business, its challenges and transitioning to alternative enterprises
Get PDFDairy farmers are grappling with serious business challenges, including rising operational costs, labour shortages, unstable milk prices, changing consumer preferences, long hours with minimal downtime and unstable weather patterns due to climate change impacts. Using a telephone-based representative survey and interviews with 147 Australian dairy farmers conducted in 2023, we employed a mixed-method approach combining quantitative and qualitative analysis to determine the challenges and primary concerns of the participants, as well as to explore potential solutions. Four key variables that contributed significantly to a binary logistic regression model of transition intentions were identified, namely: level of satisfaction with dairy farming, openness to exploring other agricultural alternatives to dairy farming, preference to receive financial and/or other support to remain in the industry and preference to receive financial and/or other support to transition into a different form of farming or business. This model accurately predicted the probability that farmers were considering transitioning away from dairy farming and the probability that they were considering staying in dairy farming. This deepens our understanding of the challenges faced by farmers in the Australian dairy industry, and provides policymakers, industry stakeholders and researchers with critical insights to facilitate transition pathways that will enhance farmers’ future sustainability
Generative AI and children’s digital futures: New research challenges
No full textFrom WALL·E (Stanton, 2008) to The Wild Robot (Sanders, 2024), children’s popular cultureis filled with sympathetic, lovable, heroic artificial intelligence (AI) and robotic characterswho make passionate and ethical choices about their own existence and help others. TheAIs of contemporary cinema and television are often excellent role models. Indeed,popular culture has been one of the main ways that AI have entered mainstreamimaginaries for more than half a century (Leaver, 2012).Yet even in popular culture, AI tend to be normative, and in most cases, coded white(Cave & Dihal, 2020). In feature films the creators of AI are similarly white male scientists the vast majority of the time (Cave et al., 2023). With the public launch of ChatGPT and other Generative AI (GenAI) tools in late 2022, AI emerged from popular culture into everyday life with surprising speed for many people, young and old. Yet the GenAI tools that have emerged bear little resemblance to the AIs of popular culture. Instead, the emerge from the same contexts and cultures which have characterised big tech for two decades, driven by commercial imperatives and extractive logics (Crawford, 2021). With GenAI being integrated into a vast array of tools and platforms – from videogames and creative software to educational platforms and almost all social media – the rapid appearance and increasing impact of AI on children and young people demands urgent attention from researchers across a broad range of fields.
Yet even in popular culture, AI tend to be normative, and in most cases, coded white (Cave & Dihal, Citation2020). In feature films the creators of AI are similarly white male scientists the vast majority of the time (Cave et al., Citation2023). With the public launch of ChatGPT and other Generative AI (GenAI) tools in late 2022, AI emerged from popular culture into everyday life with surprising speed for many people, young and old. Yet the GenAI tools that have emerged bear little resemblance to the AIs of popular culture. Instead, they emerge from the same contexts and cultures which have characterised big tech for two decades, driven by commercial imperatives and extractive logics (Crawford, Citation2021). With GenAI being integrated into a vast array of tools and platforms – from videogames and creative software to educational platforms and almost all social media – the rapid appearance and increasing impact of AI on children and young people demands urgent attention from researchers across a broad range of fields
The Routledge Handbook of Global Sustainability Education and Thinking for the 21st Century
No full textThe aim of this chapter is to explore an innovative professional learning and teaching model intended to promote First Nations’ perspectives of sustainability in Australian undergraduate courses. As part of the Yarning to Learn initiative, First Nations university educators facilitate yarning circles with non-Indigenous university educators. In these safe spaces, non-Indigenous university educators were supported to reflect on and evaluate spaces where First Nations’ voices could be heard, or amplified, through sustainability-related concepts. The program was trialled across three Australian universities: Wurundjeri (Naarm/Melbourne), Wadawurrung (Waurn Ponds) and Nyungar Countries (Boorloo/Perth). To the best of our knowledge, this model of professional learning is a first-of-a kind design, embracing yarning and yarning circles as spaces for sharing and reflecting on how to promote First Nations’ perspectives in our teaching. We draw on autoethnographic methodologies to explore both the experiences of the participating First Nations and non-Indigenous university educators. In this chapter, Yarning to Learn acts as a cross-cultural bridge to support decolonising knowledge and thinking relating to sustainability education, facilitating non-Indigenous university educators as they are mentored, supported, and by led by expert First Nations university educators. These initiatives support efforts to empower non-Indigenous university educators to decolonise teaching and promote undergraduates’ understandings of First Nations’ worldviews of caring for Country and associated sustainability-related concepts and practices
A Noble Education: Sustainability education in the 21st Century.
Get PDFThis Handbook endeavours to provide a comprehensive introduction to sustainability education as a ‘noble education’, including important sustainability contexts, the language and core concepts in sustainability thinking, key competencies for sustainability education, pedagogies and strategies for teaching sustainability, the role of ethics and stewardship responsibilities in sustainability management, and the importance of university sustainability leadership in the sustainability transition. A noble ‘sustainability-focused’ education will require educators to encourage transformative learning that highlights the life cycle impacts of our production and consumption activities on the environment, together with the development of curricula that can support the formation of a sustainability mindset that empowers sustainability action
Sustainability education in India: A discourse in education development
No full textSince the 1970s various initiatives have been introduced to bring sustainability education into the Indian curricula. However, most of these initiatives have been focused on environmental sustainability. In primary and secondary education sustainability has been incorporated into a variety of subjects. The higher education sector in India has traditionally focused on education for employment and has only recently started to include a sustainability focus. Universities initiated education for sustainability only after the introduction of the SDGs in 2015. This chapter explores the sustainability initiatives of both early school education and higher education in India. It also explores the critical interrelationship between sustainability education and values education. The data used are collected mostly from secondary data, use cases, government, NGO and university websites and research papers. The chapter has also documented some best practice that can be used as vantage points to implement education for sustainable development in India