Bringing Agriculture into the GATT: Tariffication and Rebalancing

International Relations/Trade,

Guidelines for stakeholder engagement in systematic reviews of environmental management

Abstract: People have a stake in conservation and environmental management both for their own interests and the sake of the environment itself. Environmental decision-making has changed somewhat in recent decades to account for unintentional impacts on human wellbeing. The involvement of stakeholders in environmental projects has been recognised as critical for ensuring their success and equally for the syntheses of evidence of what works, where, and for whom, providing key benefits and challenges. As a result of increased interest in systematic reviews of complex management issues, there is a need for guidance in best practices for stakeholder engagement. Here, we propose a framework for stakeholder engagement in systematic reviews/systematic maps, highlighting recommendations and advice that are critical for effective, efficient and meaningful engagement of stakeholders. The discussion herein aims to provide a toolbox of stakeholder engagement activities, whilst also recommending approaches from stakeholder engagement research that may prove to be particularly useful for systematic reviews and systematic maps

Guidelines for Operationalizing Policy Coherence for Development (PCD) as a Methodology for the Design and Implementation of Sustainable Development Strategies

Policy Coherence for Development (PCD) is considered a pillar of the 2030 Sustainable Development Agenda. It aims to promote whole of government approaches to sustainable development. Despite its prominence in development cooperation discussions, many national development professionals or stakeholders have not heard of PCD, indicating that its effectiveness is significantly limited. This article contends that the impact of PCD has not been maximized because it has been presented as a political objective or a policy tool by multilateral organizations and their member states. Instead, the article argues that PCD should be implemented as a methodology that can be adopted by domestic government and non-governmental actors alike, in order to understand trade-offs and co-benefits within and between policy sectors, thus promoting a participative approach. I-GAMMA is a research project in Mexico that examines data-driven public policy in order to promote PCD. It is based on in-depth reviews of policy documents and interviews with development actors. It is committed to open data, evidence-based policymaking, and collaborative dialogue between academics, government officials, and representatives of civil society organizations in sustainable development discussions. In the results section of this article, the project proposes participative PCD as a methodology for policy analysis through which a plurality of actors can identify mechanisms that either reinforce or undermine sustainable development strategies. This section then applies the methodology to the governance of protected natural areas in Mexico. The discussion section and the conclusions highlight the relevance of this approach for participative policymaking in sustainable development

Recognition, Reassessment, and Remediation: A small scale approach to improving harmful or exclusionary descriptive practices in the archives

Archives and special collections repositories rely on descriptive practices to provide researchers with entry points to locate and guides to navigate the materials that we steward. The efforts to remediate harmful and missing information, even in incremental steps can make a huge difference. To repair and improve our collections access and descriptions is an iterative process, and one that is scalable for any size collection or staff. This presentation will demonstrate the steps we are taking to develop a scalable project to review and assess or collections and implement changes. It will also outline the future plans to expand to diversify our reviewers to include researchers, faculty, students, and other community stakeholders

Synergistic effects of chemical mixtures: how frequent is rare?

Chemical pollution is characterised by sequential and simultaneous exposure to unintentional complex mixtures. The almost infinite number of real-life mixtures poses major challenges for investigations of all possible exposure scenarios through whole mixture or component-based approaches. As a pragmatic approach in data-poor situations, the application of a Mixture Assessment Factor to single substances assessments under REACH was announced in the European Chemicals Strategy for Sustainability. Current proposals for this factor are based on the assumption that mixtures behave additively, assuming that synergistic interactions are rare. This assumption is based on eight reviews published in the last 30 years. Synergistic deviations from additivity greater than 2-fold were reported in roughly 5% of investigated mixtures. This was more, rather than less, frequent in the handful of suitable studies of low dose mammalian mixture toxicity. This frequency is representative of mixtures toxicology studies in the literature and should not be interpreted as the frequency of synergisms in real world exposures. Understanding the frequency and likelihood of synergisms would entail detailed understanding of the co-occurrence of groups of substances giving rise to such interactions in relevant environmental media. Assumptions that synergistic interactions in real-life mixtures are rare appear to be premature. While further research is required, potential synergisms should not be omitted from debates on the conservatism or otherwise of mixture allocation factor or other regulatory approaches to protect people and environment from mixture effects

Diagnostic Applications for Micro-Synchrophasor Measurements

This report articulates and justifies the preliminary selection of diagnostic applications for data from micro-synchrophasors (µPMUs) in electric power distribution systems that will be further studied and developed within the scope of the three-year ARPA-e award titled Micro-synchrophasors for Distribution Systems

Internet innovations:exploring new horizons

The aim of this paper is to provide a standpoint on an emerging trend in sharing digital video content over the Internet. The paper is based on participative evaluative analysis of business model employed by digital video content sharing providers. The authors have found that because of wide diffusion of broadband and cheap video recording equipment, enabling digital video content to be shared online, and emerging business internet video sharing practice its users increasingly find themselves infringing the intellectual property rights of others. This has implications for anyone using online video resources. The paper offers an insight into the increasing popularity of online video and the resulting dilemmas encountered by internet researchers; it also offers a functional way for researchers, businesses and online users to understand the mechanism of infringement of the intellectual property rights relating to online video content. The paper further contributes to expanding the understanding of internet users behaviour in relation to digital video content creation and distribution in the context of challenges faced by cyberlaw

Herding of Institutional Traders

This paper sheds new light on herding of institutional investors by using a unique and superior database that identifies every transaction of financial institutions. First, the analysis reveals herding behavior of institutions. Second, the replica- tion of the analysis with low-frequent and anonymous transaction data, on which the bulk of literature is based, indicates an overestimation of herding by previous studies. Third, our results suggest that herding by large financial institutions is not intentional but results from sharing the same preference and investment style. Fourth, a panel analysis shows that herding on the sell side in stocks is positively related to past returns and past volatility while herding on the buy side is nega- tively related to past returns. In contrast to the literature, this indicates that large financial institutions do not show positive feedback strategies.Investor Behavior, Institutional Trading, Stock Prices

Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies

The significant advancements in applying Artificial Intelligence (AI) to healthcare decision-making, medical diagnosis, and other domains have simultaneously raised concerns about the fairness and bias of AI systems. This is particularly critical in areas like healthcare, employment, criminal justice, credit scoring, and increasingly, in generative AI models (GenAI) that produce synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including generative biases that affect the representation of individuals in synthetic data. This survey paper offers a succinct, comprehensive overview of fairness and bias in AI, addressing their sources, impacts, and mitigation strategies. We review sources of bias, such as data, algorithm, and human decision biases - highlighting the emergent issue of generative AI bias where models may reproduce and amplify societal stereotypes. We assess the societal impact of biased AI systems, focusing on the perpetuation of inequalities and the reinforcement of harmful stereotypes, especially as generative AI becomes more prevalent in creating content that influences public perception. We explore various proposed mitigation strategies, discussing the ethical considerations of their implementation and emphasizing the need for interdisciplinary collaboration to ensure effectiveness. Through a systematic literature review spanning multiple academic disciplines, we present definitions of AI bias and its different types, including a detailed look at generative AI bias. We discuss the negative impacts of AI bias on individuals and society and provide an overview of current approaches to mitigate AI bias, including data pre-processing, model selection, and post-processing. We emphasize the unique challenges presented by generative AI models and the importance of strategies specifically tailored to address these