Tag: Climate Change Disinformation

Abstract

Agrifood scholars have long called for changes to the dominant food system, with the goal of making food systems more sustainable and just. This paper focuses on the ways in which recent and future food system shocks provide an opportunity for sustainable transitions in the food system. However, this requires strategic engagement on the part of alternative agrifood initiatives—agrifood niches—otherwise food systems are likely to return to business as usual. Drawing on the multi-level perspective (MLP) within the sustainability transitions framework, core themes that emerge from social science studies of disasters in agrifood systems are identified. These are summarized as resources, polices, and practices that can assist niches in transforming agrifood regimes in response to disasters. The results highlight that while niches are generally independent of governments, niches would be better positioned to engage in post disaster agrifood change if they have some pre-existing connections with local or regional governments.

Abstract

Utilizing the theoretical lens of securitization and the Taiwan Democracy Ideology Securitisation conceptual framework (the TDIS), this article explains how the Tsai administration (Republic of China) transforms Taiwan democracy into the issue of security by conceptualizing Taiwan’s ideological security. In this study, I employ Tsai’s administration’s governmental discourses concerning the issues of democracy and national security to understand how political language and words support an interpretation of the social realities in which Taiwan democracy could be treated as a national security object.

The findings show that the Tsai administration conceptualized ‘ideological security’ to bring the issue of democracy security to the public. The conceptualization was handled in three main ways: forming Taiwan democracy as a dominant political ideology; spiritualizing Taiwan democracy as a political asset and revolutionarily ‘hard-earned’ achievement; and shaping Taiwan’s ideological security. Furthermore, the paper quantitatively demonstrated that the majority of the Taiwanese public agreed with the government’s transforming Taiwan democracy into ideological security.

Abstract

Human rights concerns in relation to the impacts brought forth by artificial intelligence (‘AI’) have revolved around examining how it affects specific rights, such as the right to privacy, non-discrimination and freedom of expression. However, this article argues that the effects go deeper, potentially challenging the foundational assumptions of key concepts and normative justifications of the human rights framework. To unpack this, the article applies the lens of ‘slow violence’, a term borrowed from environmental justice literature, to frame the grinding, gradual, attritional harms of AI towards the human rights framework.

The article examines the slow violence of AI towards human rights at three different levels. First, the individual as the subject of interest and protection within the human rights framework, is increasingly unable to understand nor seek accountability for harms arising from the deployment of AI systems. This undermines the key premise of the framework which was meant to empower the individual in addressing large power disparities and calling for accountability towards such abuse of power. Secondly, the ‘slow violence’ of AI is also seen through the unravelling of the normative justifications of discrete rights such as the right to privacy, freedom of expression and freedom of thought, upending the reasons and assumptions in which those rights were formulated and formalised in the first place. Finally, the article examines how even the wide interpretations towards the normative foundation of human rights, namely human dignity, is unable to address putative new challenges AI poses towards the concept. It then considers and offers the outline to critical perspectives that can inform a new model of human rights accountability in the age of AI.

Abstract

The nurturing of learners’ ways of knowing is vital for supporting their intellectual growth and their participation in democratic knowledge societies. This paper traces the development of two interrelated theoretical frameworks that describe the nature of learners’ epistemic thinking and performance and how education can support epistemic growth: the AIR and Apt-AIR frameworks. After briefly reviewing these frameworks, we discuss seven reflections on educational theory development that stem from our experiences working on the frameworks. First, we describe how our frameworks were motivated by the goal of addressing meaningful educational challenges. Subsequently, we explain why and how we infused philosophical insights into our frameworks, and we also discuss the steps we took to increase the coherence of the frameworks with ideas from other educational psychology theories. Next, we reflect on the important role of the design of instruction and learning environments in testing and elaborating the frameworks. Equally important, we describe how our frameworks have been supported by empirical evidence and have provided an organizing structure for understanding epistemic performance exhibited in studies across diverse contexts. Finally, we discuss how the development of the frameworks has been spurred by dialogue within the research community and by the need to address emerging and pressing real-world challenges. To conclude, we highlight several important directions for future research. A common thread running through our work is the commitment to creating robust and dynamic theoretical frameworks that support the growth of learners’ epistemic performance in diverse educational contexts.

Abstract

Misinformation about climate change poses a substantial threat to societal well-being, prompting the urgent need for effective mitigation strategies. However, the rapid proliferation of online misinformation on social media platforms outpaces the ability of fact-checkers to debunk false claims. Automated detection of climate change misinformation offers a promising solution. In this study, we address this gap by developing a two-step hierarchical model. The Augmented Computer Assisted Recognition of Denial and Skepticism (CARDS) model is specifically designed for categorising climate claims on Twitter. Furthermore, we apply the Augmented CARDS model to five million climate-themed tweets over a six-month period in 2022. We find that over half of contrarian climate claims on Twitter involve attacks on climate actors. Spikes in climate contrarianism coincide with one of four stimuli: political events, natural events, contrarian influencers, or convinced influencers. Implications for automated responses to climate misinformation are discussed.

Abstract

This academic paper delves into the captivating intersection of life engineering and algorithms, artificial intelligence (AI), social media, and quantitative metrics on human life, through a comprehensive review of three thought-provoking books. In each critical review, the authors add their own thoughts and impressions, as Computer Science graduates and scholars, illustrating the impact that these eye-opening books have on them. The first book, “Weapons of Math Destruction” by Cathy O’Neil, delves into the hidden dangers of algorithmic decision-making. O’Neil uncovers how algorithms can perpetuate discrimination, biases, and unfairness in domains such as education, advertising, criminal justice, employment, and finance, and emphasizes the need for ethical considerations, transparency, and human judgment in algorithmic systems. The second book, “Atlas of AI” by Kate Crawford, takes a multidimensional approach to AI beyond mere algorithms and deep learning. Crawford addresses issues such as labor exploitation, surveillance technologies, classification systems, wealth concentration, and environmental consequences due to AI. The book calls for responsible and ethical considerations in the development and usage of AI. Shoshana Zuboff’s “The Age of Surveillance Capitalism” is the third book, focusing on the pervasive influence of tech giants like Google and Facebook. Zuboff exposes the dynamics of surveillance capitalism, wherein personal data is extracted and exploited for economic gains. The book illuminates how this form of capitalism erodes privacy, reshapes societal structures, and challenges democratic norms. Illustrating the essence of these disruptive narratives and the tense dialogue taking place between ethicians or scholars and technology developers, this research examines the profound social, economic, and environmental implications brought forth by these transformative technologies. Ultimately, the paper advocates for the embrace of responsible and ethical technology development that not only safeguards the well-being of individuals but also fosters a harmonious coexistence between humans and machines amidst the winds of disruption.

Abstract

We propose a framework integrating insights from computational social science, political, and social psychology to explain how extreme polarization can occur in deeply divided societies. Extreme polarization in a society emerges through a dynamic and complex process where societal, group, and individual factors interact. Dissent at different levels of analysis represents the driver of this process, where societal-level ideological dissent divides society into opposing camps, each with contrasting collective narratives. Within these opposing camps, further dissent leads to the formation of splinter factions and radical cells—sub-groups with increasingly extreme views. At the group level, collective narratives underpinning group identity become more extreme as society fragments. At the individual level, this process involves the internalization of an extreme group narrative and norms sanctioning radical behavior. The intense bonding within these groups and the convergence of personal and group identities through identity fusion increase the likelihood of radical group behavior.

Abstract

Based on the cognitive mediation model (CMM), this study seeks to examine how attention to different media platforms influenced different knowledges via reflective integration, ultimately motivating individuals to perform corrective and restrictive actions against misinformation in the context of healthy eating misinformation. Using data collected from a national survey of 563 Chinese citizens, the findings of this study are threefold. First, attention to television and social media stimulated elaboration and interpersonal communication, while attention to websites only elicited elaboration. Second, only structural and subjective knowledge, not factual knowledge, were found to motivate individuals to perform corrective and restrictive actions. Third, a multigroup analysis demonstrated that the effects of (a) attention to TV news on elaboration, (b) attention to websites on elaboration, (c) interpersonal communication on factual knowledge, and (d) structural knowledge on restrictive actions differed among participants with different levels of altruism. Theoretically, whereas previous studies have focused on single dimension of knowledge, this study uncovered the multi-dimensional nature of knowledge by exploring factual knowledge, structural knowledge, and subjective knowledge in the CMM framework. Moreover, based on the O-S-R-O-R model, the CMM could be extended to behavioral outcomes, which have been overlooked by most CMM studies. In response, this study extends the CMM by integrating corrective and restrictive actions as behavioral outcomes. Lastly, rather than assuming individuals as homogenous in previous research, this study delves into exploring how individuals at the average age of 33.37 (SD = 8.46) with different levels of altruism engaged in different processes of cognitive mediation.

Abstract

In recent years, the study of artificial intelligence (AI) has undergone a paradigm shift. This has been propelled by the groundbreaking capabilities of generative models both in supervised and unsupervised learning scenarios. Generative AI has shown state-of-the-art performance in solving perplexing real-world conundrums in fields such as image translation, medical diagnostics, textual imagery fusion, natural language processing, and beyond. This paper documents the systematic review and analysis of recent advancements and techniques in Generative AI with a detailed discussion of their applications including application-specific models. Indeed, the major impact that generative AI has made to date, has been in language generation with the development of large language models, in the field of image translation and several other interdisciplinary applications of generative AI. Moreover, the primary contribution of this paper lies in its coherent synthesis of the latest advancements in these areas, seamlessly weaving together contemporary breakthroughs in the field. Particularly, how it shares an exploration of the future trajectory for generative AI. In conclusion, the paper ends with a discussion of Responsible AI principles, and the necessary ethical considerations for the sustainability and growth of these generative models.