Perceived Causes and Solutions to Soil Degradation in the UK and Norway

Soil quality is declining in many parts of the world, with implications for the productivity, resilience and sustainability of agri-food systems. Research suggests multiple causes of soil degradation with no single solution and a divided stakeholder opinion on how to manage this problem. However, creating socially acceptable and effective policies to halt soil degradation requires engagement with a diverse range of stakeholders who possess different and complementary knowledge, experiences and perspectives. To understand how British and Norwegian agricultural stakeholders perceived the causes of and solutions to soil degradation, we used Q-methodology with 114 respondents, including farmers, scientists and agricultural advisers. For the UK, respondents thought the causes were due to loss of soil structure, soil erosion, compaction and loss of organic matter; the perceived solutions were to develop more collaborative research between researchers and farmers, invest in training, improve trust between farmers and regulatory agencies, and reduce soil compaction. In Norway, respondents thought soils were degrading due to soil erosion, monocultures and loss of soil structure; they believed the solutions were to reduce compaction, increase rotation and invest in agricultural training. There was an overarching theme related to industrialised agriculture being responsible for declining soil quality in both countries. We highlight potential areas for land use policy development in Norway and the UK, including multi-actor approaches that may improve the social acceptance of these policies. This study also illustrates how Q-methodology may be used to co-produce stakeholder-driven policy options to address land degradation.

Progress in Recommender Systems Research: Crisis? What Crisis?

Scholars in algorithmic recommender systems research have developed a largely standardized scientific method, where progress is claimed by showing that a new algorithm outperforms existing ones on or more accuracy measures. In theory, reproducing and thereby verifying such improvements is easy, as it merely involves the execution of the experiment code on the same data. However, as recent work shows, the reported progress is often only virtual, because of a number of issues related to (i) a lack of reproducibility, (ii) technical and theoretical flaws, and (iii) scholarship practices that are strongly prone to researcher biases. As a result, several recent works could show that the latest published algorithms actually do not outperform existing methods when evaluated independently. Despite these issues, we currently see no signs of a crisis, where researchers re-think their scientific method, but rather a situation of stagnation, where researchers continue to focus on the same topics. In this paper, we discuss these issues, analyze their potential underlying reasons, and outline a set of guidelines to ensure progress in recommender systems research.

Using Adaptive Capacity to Shift Absorptive Capacity: A Framework of Water Reallocation in Highly Modified Rivers

Damming and water regulation creates highly modified rivers with limited ecosystem integrity and resilience. This, coupled with an ongoing global biodiversity crisis, makes river restoration a priority, which requires water reallocation. Coupled human–natural systems research provides a suitable lens for integrated systems’ analysis but offers limited insight into the governance processes of water reallocation. Therefore, we propose an analytical framework, which combines insight from social–hydrological resilience and water reallocation research, and identifies the adaptive capacity in highly modified rivers as the capacity for water reallocation. We test the framework by conducting an analysis of Sweden, pre- and post-2019, a critical juncture in the governance of the country’s hydropower producing rivers. We identify a relative increase in adaptive capacity post- 2019 since water reallocation is set to occur in smaller rivers and tributaries, while leaving large-scaled rivers to enjoy limited water reallocation, or even increased allocation to hydropower. We contend that the proposed framework is broad enough to be of general interest, yet sufficiently specific to contribute to the construction of middle-range theories, which could further our understanding of why and how governance processes function, change, and lead to outcomes in terms of modified natural resource management and resilience shifts.

Deep Transfer Learning & Beyond: Transformer Language Models in Information Systems Research

AI is widely thought to be poised to transform business, yet current perceptions of the scope of this transformation may be myopic. Recent progress in natural language processing involving transformer language models (TLMs) offers a potential avenue for AI-driven business and societal transformation that is beyond the scope of what most currently foresee. We review this recent progress as well as recent literature utilizing text mining in top IS journals to develop an outline for how future IS research can benefit from these new techniques. Our review of existing IS literature reveals that suboptimal text mining techniques are prevalent and that the more advanced TLMs could be applied to enhance and increase IS research involving text data, and to enable new IS research topics, thus creating more value for the research community. This is possible because these techniques make it easier to develop very powerful custom systems and their performance is superior to existing methods for a wide range of tasks and applications. Further, multilingual language models make possible higher quality text analytics for research in multiple languages. We also identify new avenues for IS research, like language user interfaces, that may offer even greater potential for future IS research.