In many economies, youth unemployment rates over the past two decades have exceeded 10 percentage points, highlighting that not all youth successfully transition successfully from schooling to employment. Equally disturbing are the high rates of young adults not observed in employment, education, or training, a rate commonly referred to as “NEET.” There is not a single pathway for successful transitions. Understanding these pathways and the influences of geographic location, employment opportunities, and family and community characteristics that contribute to positive transitions is crucial. While abundant data exists to support this understanding, it is often siloed and not easily combined to inform schools, communities, and policymakers about effective strategies and necessary changes. Researchers prefer working with datasets, while many stakeholders favor results presented through storytelling and visualizations. This paper introduces YouthView, an innovative online platform designed to provide comprehensive insights into youth transition challenges and opportunities. YouthView integrates information from datasets on youth disadvantage indicators, employment, skills demand, and job vacancy at regional levels. The platform features two modes: a guided storytelling mode with selected visualizations, and an open-ended suite of exploratory dashboards for in-depth data analysis. This dual approach enables policymakers, community organizations, and education providers to gain a nuanced understanding of the challenges faced by different communities. By illuminating spatial patterns, socioeconomic disparities, and relationships between disadvantage factors and labor market dynamics, YouthView facilitates informed decision-making and the development of targeted interventions, ultimately contributing to improved youth economic outcomes and expanded opportunities in areas of greatest need.

Embracing the potential of foresight in migration policy, North Macedonia has embarked on a ground-breaking journey to institutionalize anticipatory governance through extensive capacity-building activities, imparting foresight methods to stakeholders responsible for shaping migration policies. This research provides a comprehensive overview, detailing the initiative’s origins, alignment with the Resolution on Migration Policy 2021–2025, and the accompanying Action Plan. The study assesses the impact and potential of the Anticipatory Governance in Migration in North Macedonia when fully integrated with the action plan, which focuses on data-based management that oversees the migration policy resolution and the migration policy milieu. Through a comprehensive analysis of the foresight interventions, training programs, and stakeholder engagements, this study unveils the potential impact of forward-looking planning on North Macedonia’s migration policy landscape. The conclusion and recommendations have broader significance, extending beyond North Macedonia to serve as a model for other countries confronting migration challenges in our rapidly changing world.

There is a growing attention towards personalised digital health interventions such as health apps. These often depend on the collection of sensitive personal data, which users generally have limited control over. This work explores perspectives on data sharing and health apps in two different policy contexts, London and Hong Kong. Through this study, our goal is to generate insight about what digital health futures should look like and what needs to be done to achieve them. Using a survey based on a hypothetical health app, we considered a range of behavioural influences on personal health data sharing with the Capability, Opportunity, Motivation model of Behaviour (COM-B) to explore some of the key factors affecting the acceptability of data sharing. Results indicate that willingness to use health apps is influenced by users’ data literacy and control, comfort with sharing health and location data, existing health concerns, access to personalised health advice from a trusted source, and willingness to provide data access to specific parties. Gender is a statistically significant factor, as men are more willing to use health apps. Survey respondents in London are statistically more willing to use health apps than respondents in Hong Kong. Finally, we propose several policy approaches to address these factors, which include the co-creation of standards for using artificial intelligence (AI) to generate health advice, innovating app design and governance models that allow users to carefully control their data, and addressing concerns of gender-specific privacy risks and public trust in institutions dealing with data.

The integration of artificial intelligence (AI)-driven technologies into peace dialogues offers both innovative possibilities and critical challenges for contemporary peacebuilding practice. This article proposes a context-sensitive taxonomy of digital deliberation tools designed to guide the selection and adaptation of AI-assisted platforms in conflict-affected environments. Moving beyond static typologies, the framework accounts for variables such as scale, digital literacy, inclusivity, security, and the depth of AI integration. By situating digital peace dialogues within broader peacebuilding and digital democracy frameworks, the article examines how AI can enhance participation, scale deliberation, and support knowledge synthesis, —while also highlighting emerging concerns around algorithmic bias, digital exclusion, and cybersecurity threats. Drawing on case studies involving the United Nations (UN) and civil society actors, the article underscores the limitations of one-size-fits-all approaches and makes the case for hybrid models that balance AI capabilities with human facilitation to foster trust, legitimacy, and context-responsive dialogue. The analysis contributes to peacebuilding scholarship by engaging with the ethics of AI, the politics of digital diplomacy, and the sustainability of technological interventions in peace processes. Ultimately, the study argues for a dynamic, adaptive approach to AI integration, continuously attuned to the ethical, political, and socio-cultural dimensions of peacebuilding practice.

The emergence of large language models has significantly expanded the use of natural language processing (NLP), even as it has heightened exposure to adversarial threats. We present an overview of adversarial NLP with an emphasis on challenges, policy implications, emerging areas, and future directions. First, we review attack methods and evaluate the vulnerabilities of popular NLP models. Then, we review defense strategies that include adversarial training. We describe major policy implications, identify key trends, and suggest future directions, such as the use of Bayesian methods to improve the security and robustness of NLP systems.

European asylum policy still has a long way to go to better address protection challenges. This paper presents data and visualizations that should help improve responsibility-sharing and solidarity between states. We developed an interactive cartographic tool to map the distribution of refugees in Europe. Besides the observed geographic distribution of asylum seekers and beneficiaries of the temporary protection status, our tool allows for the calculation of a theoretical distribution between countries based on different criteria. The tool is an interactive visualization created with the software “Tableau Desktop.” The original data was collected from Eurostat and the World Bank, before being processed by the research team with the Extract Transform Load (ETL) utility “Tableau Prep” and made available through the Tableau Desktop application. The actual number of asylum applications lodged in country A can thus be compared with the number that would be proportional to that country’s population within Europe in combination with three other criteria. Maps of observed and theoretical reallocations can thus be produced based on population size, area, unemployment rate, economic prosperity or a mix of these factors. The number of refugees received is represented by a red semicircle while the “equitable” number in proportion to given criteria is represented by a grey semicircle. Our database not only allows geographical analysis of the drivers of refugee distribution in Europe, but it also provides the population and policymakers with a solid basis for discussing responsibility-sharing schemes, such as those envisaged in the new EU Asylum Pact of 2024.

As social media continues to grow, understanding the impact of storytelling on stakeholder engagement becomes increasingly important for policymakers and organizations who wish to influence policymaking. While prior research has explored narrative strategies in advertising and branding, researchers have paid scant attention to the specific influence of stories on social media stakeholder engagement. This study addresses this gap by employing Narrative Transportation Theory (NTT) and leveraging Natural Language Processing (NLP) to analyze the intricate textual data generated by social media platforms. The analysis of 85,075 Facebook publications from leading Canadian manufacturing companies, using Spearman’s rank correlation coefficient, underscores that individual storytelling components—character, sequence of events, and setting—along with the composite narrative structure significantly enhance stakeholder engagement. This research contributes to a deeper understanding of storytelling dynamics in social media, emphasizing the importance of crafting compelling stories to drive meaningful stakeholder engagement in the digital realm. The results of our research can prove useful for those who wish to influence policymakers or for policymakers who want to promote new policies.

Over 193 countries have signed at least one of more than 500 multilateral treaties addressing critical global issues, such as human rights, environmental protection, and trade. Ratifying a treaty obligates a country, as a “State Party,” to report to the United Nations on its progress toward implementing the treaty’s provisions. These reports and their associated review processes generate a wealth of textual data. Effectively monitoring, reviewing, and assessing national, regional, and global progress toward these treaty commitments is crucial for ensuring compliance and realizing the benefits of international cooperation. The UN Convention on the Rights of Persons with Disabilities (CRPD), which has been ratified by 191 countries, exemplifies this challenge. With over 1.3 billion people worldwide living with disabilities, the CRPD aims to promote a shift from a charity-based “medical model” that views disability as an individual deficiency, to a rights-based “social justice model” that emphasizes societal barriers and inclusivity. Each State Party submits periodic reports to the Committee on the Rights of Persons with Disabilities detailing their implementation efforts. This study analyzed all available CRPD State Reports (N = 170) using text mining, Natural Language Processing, and GenerativeAI tools to assess global progress, identify regional variations, and explore the factors influencing successful implementation. The findings reveal evidence of widespread CRPD implementation, growing support for social justice and economic inclusion, and the importance of civil society engagement. Hybrid data analysis approach of this study offers a promising framework for harnessing the power of textual data to advance the realization of treaty commitments worldwide.

In our digital world, reusing data to inform: decisions, advance science, and improve people’s lives should be easier than ever. However, the reuse of data remains limited, complex, and challenging. Some of this complexity requires rethinking consent and public participation processes about it. First, to ensure the legitimacy of uses, including normative aspects like agency and data sovereignty. Second, to enhance data quality and mitigate risks, especially since data are proxies that can misrepresent realities or be oblivious to the original context or use purpose. Third, because data, both as a good and infrastructure, are the building blocks of both technologies and knowledge of public interest that can help societies work towards the well-being of their people and the environment. Using the case study of the European Health Data Space, we propose a multidimensional, polytopic framework with multiple intersections to democratising decision-making and improving the way in which meaningful participation and consent processes are conducted at various levels and from the point of view of institutions, regulations, and practices.

Although ‘in-the-wild’ technology testing provides an important opportunity to collect evidence about the performance of new technologies in real world deployment environments, such tests may themselves cause harm and wrongfully interfere with the rights of others. This paper critically examines real-world AI testing, focusing on live facial recognition technology (FRT) trials by European law enforcement agencies (in London, Wales, Berlin, and Nice) undertaken between 2016 and 2020, which serve as a set of comparative case studies. We argue that there is an urgent need for a clear framework of principles to govern real-world AI testing, which is currently a largely ungoverned ‘wild west’ without adequate safeguards or oversight. We propose a principled framework to ensure that these tests are undertaken in an epistemically, ethically, and legally responsible manner, thereby helping to ensure that such tests generate sound, reliable evidence while safeguarding the human rights and other vital interests of others. Although the case studies of FRT testing were undertaken prior to the passage of the EU’s AI Act, we suggest that these three kinds of responsibility should provide the foundational anchor points to inform the design and conduct of real-world testing of high-risk AI systems pursuant to Article 60 of the AI Act.

The rise of visually driven platforms like Instagram has reshaped how information is shared and understood. This study examines the role of social, cultural, and political (SCP) symbols in Instagram posts during Taiwan’s 2024 election, focusing on their influence in anti-misinformation efforts. Using large language models (LLMs)—GPT-4 Omni and Gemini Pro Vision—we analyzed thousands of posts to extract and classify symbolic elements, comparing model performance in consistency and interpretive depth. We evaluated how SCP symbols affect user engagement, perceptions of fairness, and content spread. Engagement was measured by likes, while diffusion patterns followed the SEIZ epidemiological model. Findings show that posts featuring SCP symbols consistently received more interaction, even when follower counts were equal. Although political content creators often had larger audiences, posts with cultural symbols drove the highest engagement, were perceived as more fair and trustworthy, and spread more rapidly across networks. Our results suggest that symbolic richness influences online interactions more than audience size. By integrating semiotic analysis, LLM-based interpretation, and diffusion modeling, this study offers a novel framework for understanding how symbolic communication shapes engagement on visual platforms. These insights can guide designers, policymakers, and strategists in developing culturally resonant, symbol-aware messaging to combat misinformation and promote credible narratives.

The capabilities of large language models (LLMs) have advanced to the point where entire textbooks can be queried using retrieval-augmented generation (RAG), enabling AI to integrate external, up-to-date information into its responses. This study evaluates the ability of two OpenAI models, GPT-3.5 Turbo and GPT-4 Turbo, to create and answer exam questions based on an undergraduate textbook. 14 exams were created with four true-false, four multiple-choice, and two short-answer questions derived from an open-source Pacific Studies textbook. Model performance was evaluated with and without access to the source material using text-similarity metrics such as ROUGE-1, cosine similarity, and word embeddings. Fifty-six exam scores were analyzed, revealing that RAG-assisted models significantly outperformed those relying solely on pre-trained knowledge. GPT-4 Turbo also consistently outperformed GPT-3.5 Turbo in accuracy and coherence, especially in short-answer responses. These findings demonstrate the potential of LLMs in automating exam generation while maintaining assessment quality. However, they also underscore the need for policy frameworks that promote fairness, transparency, and accessibility. Given regulatory considerations outlined in the European Union AI Act and the NIST AI Risk Management Framework, institutions using AI in education must establish governance protocols, bias mitigation strategies, and human oversight measures. The results of this study contribute to ongoing discussions on responsibly integrating AI in education, advocating for institutional policies that support AI-assisted assessment while preserving academic integrity. The empirical results suggest not only performance benefits but also actionable governance mechanisms, such as verifiable retrieval pipelines and oversight protocols, that can guide institutional policies.

Following the large-scale Russian invasion in February 2022, policymakers and humanitarian actors urgently sought to anticipate displacement flows within Ukraine. However, existing internal displacement data systems had not been adapted to contexts as dynamic as a full-fledged war marked by uneven trigger events. A year and a half later, policymakers and practitioners continue to seek forecasts, needing to anticipate how many internally displaced persons (IDPs) can be expected to return to their areas of origin and how many will choose to stay and seek a durable solution in their place of displacement. This article presents a case study of an anticipatory approach deployed by the International Organization for Migration (IOM) Mission in Ukraine since March 2022, delivering nationwide displacement figures less than 3 weeks following the invasion alongside near real-time data on mobility intentions as well as key data anticipating the timing, direction, and volume of future flows and needs related to IDP return and (re)integration. The authors review pre-existing mobility forecasting approaches, then discuss practical experiences with mobility prediction applications in the Ukraine response using the Ukraine General Population Survey (GPS), including in program and policy design related to facilitating durable solutions to displacement. The authors focus on the usability and ethics of the approach, already considered for replication in other displacement contexts.

Achieving Zero Hunger by 2030, a United Nations Sustainable Development Goal, requires resilient food systems capable of securely feeding billions. This article introduces the Food Systems Resilience Score (FSRS), a novel framework that adapts a proven resilience measurement approach to the context of food systems. The FSRS builds on the success of the Community Flood Resilience Measurement Tool, which has been used in over 110 communities, by applying its five capitals (natural, human, social, financial, and manufactured) and four qualities (robustness, redundancy, resourcefulness, and rapidity) framework to food systems. We define food system resilience as the capacity to ensure adequate, appropriate, and accessible food supply to all, despite various disturbances and unforeseen disruptions. The FSRS measures resilience across multiple dimensions using carefully selected existing indicators, ensuring broad applicability and comparability. Our methodology includes rigorous technical validation to ensure reliability, including optimal coverage analysis, stability checks, and sensitivity testing. By providing standardized metrics and a comprehensive assessment of food system resilience, this framework not only advances research but also equips policymakers with valuable tools for effective interventions. The FSRS enables comparative analysis between countries and temporal tracking of resilience changes, facilitating targeted strategies to build and maintain resilient national food systems. This work contributes to the global effort toward long-term food security and sustainability.

Federated learning (FL) is a machine learning technique that distributes model training to multiple clients while allowing clients to keep their data local. Although the technique allows one to break free from data silos keeping data local, to coordinate such distributed training, it requires an orchestrator, usually a central server. Consequently, organisational issues of governance might arise and hinder its adoption in both competitive and collaborative markets for data. In particular, the question of how to govern FL applications is recurring for practitioners. This research commentary addresses this important issue by inductively proposing a layered decision framework to derive organisational archetypes for FL’s governance. The inductive approach is based on an expert workshop and post-workshop interviews with specialists and practitioners, as well as the consideration of real-world applications. Our proposed framework assumes decision-making occurs within a black box that contains three formal layers: data market, infrastructure, and ownership. Our framework allows us to map organisational archetypes ex-ante. We identify two key archetypes: consortia for collaborative markets and in-house deployment for competitive settings. We conclude by providing managerial implications and proposing research directions that are especially relevant to interdisciplinary and cross-sectional disciplines, including organisational and administrative science, information systems research, and engineering.

This study explores the potential of applying machine learning (ML) methods to identify and predict areas at risk of food insufficiency using a parsimonious set of publicly available data sources. We combine household survey data that captures monthly reported food insufficiency with remotely sensed measures of factors influencing crop production and maize price observations at the census enumeration area (EA) in Malawi. We consider three machine-learning models of different levels of complexity suitable for tabular data (TabNet, random forests, and LASSO) and classical logistic regression and examine their performance against the historical occurrence of food insufficiency. We find that the models achieve similar accuracy levels with differential performance in terms of precision and recall. The Shapley additive explanation decomposition applied to the models reveals that price information is the leading contributor to model fits. A possible explanation for the accuracy of simple predictors is the high spatiotemporal path dependency in our dataset, as the same areas of the country are repeatedly affected by food crises. Recurrent events suggest that immediate and longer-term responses to food crises, rather than predicting them, may be the bigger challenge, particularly in low-resource settings. Nonetheless, ML methods could be useful in filling important data gaps in food crises prediction, if followed by measures to strengthen food systems affected by climate change. Hence, we discuss the tradeoffs in training these models and their use by policymakers and practitioners.

Data governance has emerged as a pivotal area of study over the past decade, yet despite its growing importance, a comprehensive analysis of the academic literature on this subject remains notably absent. This paper addresses this gap by presenting a systematic review of all academic publications on data governance from 2007 to 2024. By synthesizing insights from more than 3500 documents authored by more than 9000 researchers across various sources, this study offers a broad yet detailed perspective on the evolution of data governance research.

In recent decades, researchers have analyzed professional military education (PME) organizations to understand the characteristics and transformation of the core of military culture, the officer corps. Several historical studies have demonstrated the potential of this approach, but they were limited by both theoretical and methodological hurdles. This paper presents a new historical-institutionalist framework for analyzing officership and PME, integrating computational social science methods for large-scale data collection and analysis to overcome limited access to military environments and the intensive manual labor required for data collection and analysis. Furthermore, in an era where direct demographic data are increasingly being removed from the public domain, our indirect estimation methods provide one of the few viable alternatives for tracking institutional change. This approach will be demonstrated using web-scraping and a quantitative text analysis of the entire repository of theses from an elite American military school.