Month: August 2024

Abstract

In this article, we argue that YouTube’s algorithm should be programmed to make a modest but significant percentage (e.g. 2%) of recommendations for the climate. Just as a librarian has a (meta-editorial) responsibility to highlight certain titles and not others, we believe that so should YouTube’s algorithm. The company, we argue, has duties of content moderation, reparation and meta-editing, as well as strong consequentialist reasons to program its algorithm to do so. With 2 billion users, our proposed intervention could be an effective contribution to mitigating the climate crisis in a transparent and accountable way. We consider different setups, with varying degrees of transparency and centralization. We then address the worries that such a project may raise: the risk of manipulation, the threat of a slippery slope, and the concerns for freedom of expression. We conclude that none of these elements seriously undermine the desirability of our proposal.

Abstract

Integrating adversarial machine learning with question answering (QA) systems has emerged as a critical area for understanding the vulnerabilities and robustness of these systems. This article aims to review adversarial example-generation techniques in the QA field, including textual and multimodal contexts. We examine the techniques employed through systematic categorization, providing a structured review. Beginning with an overview of traditional QA models, we traverse the adversarial example generation by exploring rule-based perturbations and advanced generative models. We then extend our research to include multimodal QA systems, analyze them across various methods, and examine generative models, seq2seq architectures, and hybrid methodologies. Our research grows to different defense strategies, adversarial datasets, and evaluation metrics and illustrates the literature on adversarial QA. Finally, the paper considers the future landscape of adversarial question generation, highlighting potential research directions that can advance textual and multimodal QA systems in the context of adversarial challenges.

Abstract

To maximize our teacher candidates’ learning about culturally and linguistically diverse students, we developed and implemented Case-Based Instructional (CBI) Modules (Language, Identity, Family, Assumptions) in two teacher preparation courses at a US university. We examined the Modules’ impacts on teacher candidates’ learning, self-efficacy, attitudes, and transfer of learning to novel contexts. Examining the Modules’ effectiveness within and across two delivery modes indicated that both instructor-facilitated and instructor-supported approaches to CBI elicit similar positive attitudes and are effective in enhancing teacher candidates’ learning, but not transfer. When teacher candidates’ analyses of cases were not facilitated by instructor, however, there were some missed opportunities for learning.

Abstract

COVID-19 impacted citizens around the globe physically, economically, socially, or emotionally. In the first 2 years of its emergence, the virus dominated media in offline and online conversations. While fear was a justifiable emotion; were online discussions deliberately fuelling it? Concerns over the prominent negativity and mis/disinformation on social media grew, as people relied on social media more than ever before. This study examines expressions of stress and emotions used by bots on what was formerly known as Twitter. We collected 5.6 million tweets using the term “Coronavirus” over two months in the early stages of the pandemic. Out of 77,432 active users, we found that over 15% were bots while 48% of highly active accounts displayed bot-like behaviour. We provide evidence of how bots and humans used language relating to stress, fear and sadness; observing substantially higher prevalence of stress and fear messages being re-tweeted by bots over human accounts. We postulate, social media is an emotion-driven attention information market that is open to “automated” manipulation, where attention and engagement are its primary currency. This observation has practical implications, especially online discussions with heightened emotions like stress and fear may be amplified by bots, influencing public perception and sentiment.

Abstract

Long COVID represents the constellation of post-acute and long-term health effects caused by SARS-CoV-2 infection; it is a complex, multisystem disorder that can affect nearly every organ system and can be severely disabling. The cumulative global incidence of long COVID is around 400 million individuals, which is estimated to have an annual economic impact of approximately $1 trillion—equivalent to about 1% of the global economy. Several mechanistic pathways are implicated in long COVID, including viral persistence, immune dysregulation, mitochondrial dysfunction, complement dysregulation, endothelial inflammation and microbiome dysbiosis. Long COVID can have devastating impacts on individual lives and, due to its complexity and prevalence, it also has major ramifications for health systems and economies, even threatening progress toward achieving the Sustainable Development Goals. Addressing the challenge of long COVID requires an ambitious and coordinated—but so far absent—global research and policy response strategy. In this interdisciplinary review, we provide a synthesis of the state of scientific evidence on long COVID, assess the impacts of long COVID on human health, health systems, the economy and global health metrics, and provide a forward-looking research and policy roadmap.

Abstract

Increasingly, universities have been seen as sites for practicing decolonization work. Examples include the introduction of Land-based curricula, tribal relationship building, and the offering of critical Indigenous studies courses. However, universities remain spaces with deep colonial foundations. This paper offers a description of the challenges and insights gained through attempted decolonial reconcili-action work within this imperfect environment. We critically examine the conception, implementation and lasting impact of a course offered at Western Washington University (WWU), located in Washington State on the ancestral territory of the Lummi and Nooksack peoples. The “Socio-ecology and Reconcili-action in the Northern Salish Sea” course wove together Land-based learning and relationship-building to engage students in reconciliation. We worked specifically with the ɬaʔəmen (Tla’amin) Nation, located in British Columbia, and included classroom and virtual work in Bellingham and a field trip to the Nation’s traditional territory near qathet Regional District (so called Powell River). Two settler students and a settler instructor reflect on the course through a series of reflexive vignettes culminating in a list of learning commitments: to learn from a diversity of peoples, especially Indigenous community members; to learn with gratitude, respect, and reciprocity, and without fear of making mistakes; and to actively apply our knowledge to further reconciliation and decolonization. These commitments are offered as a starting point for other members of the higher education community who recognize their responsibility to advance reconciliation and decolonization.