Social Sciences

Abstract: Innovation systems are commonly analysed through indicators of investment, performance, output, and technological capacity. However, less attention has been paid to the composition of the research workforce as a structural dimension of system organisation. This article examines the relationship between innovation system capacity and workforce composition, focusing on the share of women among researchers as an empirically observable indicator of participation within national R&D systems. The study develops a configurational systems perspective in which innovation systems are understood as multidimensional arrangements rather than linear rankings. System capacity is operationalised through a synthetic coordinate combining R&D intensity, innovation performance, and R&D expenditure per researcher, while workforce composition is treated as a separate analytical dimension. Using comparative cross-national data, the article combines correlation analysis, coordinate-based mapping, and quadrant-based interpretation to examine whether participation patterns are systematically related to the structural capacity of national innovation systems. The findings indicate a recurrent inverse association between innovation system capacity and the relative share of women among researchers. Higher-capacity systems are more frequently positioned in configurations characterised by narrower participation, while broader participation is more often observed in lower- or intermediate-capacity systems. The results also suggest the presence of a transition zone at intermediate levels of system capacity, where participation patterns become more differentiated and system trajectories diverge. The article does not interpret this relationship as a deterministic causal law. Rather, it identifies a recurrent structural regularity that deserves further theoretical and empirical investigation. The contribution of the study is threefold: first, it integrates workforce composition into the systems analysis of innovation; second, it shows how configurational methods can reveal structural relationships that remain obscured in composite rankings; and third, it highlights the need to assess innovation systems not only by their capacity and performance, but also by the participation structures through which knowledge production is organised.

Abstract: India’s artificial intelligence (AI) policy landscape has rapidly matured from an aspirational innovation strategy into a deeply layered governance architecture. This framework now encompasses a nationwide development strategy. The macro-level policy trajectory remains consistent: catalyze domestic innovation, leverage systems for developmental objectives, and consciously avoid ex-ante regulations that might stifle the domestic ecosystem. India relies on a pragmatic matrix of existing statutory laws, targeted legislative amendments, voluntary industry standards, and progressive institutional risk management capacity-building. This architecture possesses substantial structural strengths, particularly in its attention to socio-economic inclusion, public-interest, state capacity enhancement, and the unique utilization of Digital Public Infrastructure (DPI). Yet, it domain contains unresolved internal structural tensions. While India’s foundational strategy and guidance documents consistently employ a rights-affirming vocabulary, the operative statutory mechanisms and enforcement pathways frequently create an architecture where innovation policy appears decoupled from enforceable civil rights, and state discretion is more robustly specified than safeguards. This paper presents a critical reading of this landscape, evaluating its primary strengths, internal systemic contradictions, legal-policy gaps, and the practical difficulties likely to arise during implementation on the ground.

Abstract: Local taxation is often examined as a technical component of public finance, but its administrative significance also depends on how citizens connect local taxes with visible public services, institutional transparency and fiscal trust. This study analyses Romania as a Central and Eastern European (CEE) case of local fiscal governance and asks how local public services, fiscal trust and tax morale shape the conditions for voluntary tax compliance. The article uses a comparative conceptual-empirical design based on a harmonized dataset for eleven CEE countries over 2014–2024. The empirical core relies on Eurostat and European Commission fiscal indicators, while Eurobarometer and WVS/QoG indicators are used contextually for trust and tax morale. The findings show that Romania is broadly aligned with the CEE average in local government revenue and expenditure, but records substantially lower values for local tax revenue as a share of Gross domestic product (GDP), total tax revenue and local government revenue. Recurrent taxes on immovable property remain relatively visible, although their fiscal weight declined over time. The article contributes by reframing local taxation as a governance relationship, showing that voluntary compliance conditions depend not only on fiscal rules or enforcement, but also on local fiscal autonomy, visible services, transparency and trust.

Abstract: This paper examines the datafication of migrant labor governance in Malaysia, arguing that the growing digital infrastructure surrounding migration — biometric registration, employer-tied databases, algorithmic productivity monitoring, and cross-border recruitment platforms — constitutes a regime of digital dispossession that is legally unaddressed and politically uncontested. Drawing on critical data studies, decolonial political economy, and the emerging field of digital migration studies, the paper interrogates how migrant workers in Malaysia are rendered exhaustively visible to state and capital through data, while remaining structurally invisible to the law that is supposed to protect them. Anchored in Chowdhury's (2022, 2023, 2026a, 2026b) frameworks of reciprocal methodology and Indigenous Gnoseology, and engaging with Couldry and Mejias's (2019) concept of data colonialism, Spanger and Andersen's (2023) analysis of convoluted mobility, Leurs and Smets's (2018) digital migration studies framework, and Beduschi's (2021) international human rights analysis of AI-driven migration management, the paper develops a concept of relational data sovereignty as an alternative governance foundation. It proposes five institutional reforms for Malaysia and ASEAN and argues that the reform of digital governance in migration contexts requires both epistemological and institutional transformation — centering the knowledge, agency, and rights of those most governed by data and least protected by law.

Abstract: Artificial Intelligence (AI) is increasingly embedded in public governance, raising questions about how institutions can anticipate its societal implications while safeguarding democratic accountability amid expanding computational infrastructures. This article examines how anticipatory AI governance can be operationalised in the age of super-computing through a mixed-methods multistakeholder approach in the Basque Country (Spain). The study focuses on the city-regional governance setting of Gipuzkoa, a de-volved historical territory with fiscal autonomy and a growing advanced-computing ecosystem centred in Donostia–San Sebastián, where regional initiatives are positioning the Basque Country as an emerging “quantum territory” within Europe’s high-performance and quantum computing landscape, including the installation of IBM Quantum System Two. Methodologically, the study combines action research with three stakeholder groups and a quantitative online survey of citizens (N = 911). The action research engaged six civil society organisations, seven provincial directorates, and eleven municipalities. Results indicate that city-regional administrations can function as labor-atories for public AI governance when policy experimentation is combined with empirical evidence and advanced computational infrastructures. The findings suggest policy recommendations for supercomputing ecosystems, including transparent AI experi-mentation, public-interest data governance, and policy sandboxes linking advanced computing, civic participation, and accountable digital public services.

Abstract: The rapid advancement of artificial intelligence (AI) presents unprecedented challenges for labor market forecasting, requiring fundamental methodological innovations that move beyond traditional extrapolation techniques. This policy paper proposes comprehensive enhancements to the U.S. Bureau of Labor Statistics (BLS) employment projection systems to better capture and forecast AI's impact on employment structures, job roles, and workforce skill requirements. Drawing on recent empirical research and the bureau's existing methodological frameworks, we present an integrated architectural framework that combines task-based exposure modeling, real-time data analytics, causal inference methods, and enhanced gross flows estimation. Our recommendations address critical gaps in current BLS methodologies identified through systematic literature review and analysis of emerging AI adoption patterns, including the distinction between automation and augmentation effects, the nonlinear dynamics of AI adoption, and differential impacts across worker demographics. We propose a dynamic Occupational AI Exposure Score (OAIES) framework that leverages large language models and occupational task data, alongside enhanced data collection strategies and modernized estimation techniques. The architectural framework, illustrated through five interconnected diagrams, demonstrates how these methodological innovations integrate into a coherent system for measuring labor market transformation. These enhancements would enable more accurate projections of job displacement, skill evolution, and employment transformation across industries and geographic regions, supporting evidence-based policymaking for workforce development in an AI-driven economy. The paper concludes with a phased implementation strategy and validation protocol to ensure methodological rigor and operational feasibility.

Abstract: The European Green Deal places environmental taxation at the centre of decarbonisation policies. Nevertheless, the empirical evidence for its effectiveness as an incentive for capital eco-investments remains limited, particularly at the sectoral level. The present study analyses this relationship through a country-sector panel of seven EU member states and four sectors under NACE Rev.2 for the period 2014-2023. A five-step empirical strategy is employed, comprising: descriptive statistics, correlation analysis with relative indicators, fixed-effects panel regressions, the Granger causality test, and robustness checks. The results demonstrate a clear scale effect - the correlation between the absolute values of environmental taxes and eco-investments is very high, yet following normalisation against the scale of the economy it becomes practically zero and statistically insignificant. The panel regressions likewise establish no statistically significant relationship, and the Granger test does not confirm causality in either direction. The robustness checks confirm this finding. On this basis, the study concludes that environmental taxation in isolation does not stimulate sectoral eco-investments and functions rather as a fiscal instrument without a discernible investment effect. The findings suggest the need for a policy rethink through more targeted revenue use, sectoral differentiation, and the combining of tax instruments with non-fiscal mechanisms for more effective management of transition financial risk.

Abstract: This paper presents a comprehensive analysis of artificial intelligence (AI) adoption and governance frameworks in New Jersey, examining the state's strategic initiatives to become a national leader in AI innovation while ensuring ethical implementation and public trust. Through systematic review of recent developments including the $500 million Next New Jersey Program, establishment of the NJ AI Hub with founding partners Princeton University, Microsoft, and CoreWeave, and implementation of workforce training initiatives reaching over 65,000 -75,000 state employees, we analyze how governance structures can accelerate responsible AI adoption. Our research synthesizes findings from the New Jersey AI Task Force report, academic literature from Rutgers and Princeton, and industry implementations from leading technology providers to develop a multi-layered governance framework tailored to New Jersey's unique public-private-academic ecosystem. Key findings indicate that integrated approaches combining infrastructure investment, workforce development, and ethical guidelines yield optimal outcomes, with 60-70% of New Jersey adults now engaging with AI tools and over 1,200 - 1,500 jobs created in AI-related fields. The paper proposes actionable recommendations for policymakers, including standardized AI procurement protocols, cross-agency coordination mechanisms, and continuous stakeholder engagement strategies. This work contributes to both theoretical understanding of AI governance at the state level and practical guidance for jurisdictions seeking to balance innovation acceleration with responsible oversight, while addressing emerging challenges in agentic AI systems and algorithmic discrimination prevention.

Abstract: Opposing historical nihilism is an important proposition that runs through the century-long ideological struggle of the Communist Party of China. As an erroneous trend that denies the achievements of China's revolution, construction, and reform, historical nihilism has long been highly vigilant and continuously criticized by the Communist Party of China. Based on methods such as literature analysis and historical evidence verification, this paper analyzes the origins and evolution of historical nihilism, traces the Party's practical journey in opposing historical nihilism during the New Democratic Revolution period, the Socialist Revolution and Construction period, and the Reform and Opening-up new era, and reveals the theoretical responses and practical innovations of Chinese Communists in the new era against historical nihilism.

Abstract: The effect of political Governor or party on Renewable Portfolio Standard adoption is examined across 50 U.S. states from 2001 to 2024. Using fixed-effects panel regression with clustered standard errors, it is found that persistent Democratic control has a small positive and statistically insignificant effect on RPS adoption (β = 0.047, SE = 0.045). While lagged party control shows a statistically significant impact. Specifically, two(β =0.004) and three(β =0.006) year Democratic control lag is associated with higher RPS levels compared to Republican control, which affirms policy lag hypothesis. Electricity prices show a positive result with RPS (pre-2009: β = 0.035, p < 0.05; post-2009: β = 0.018, p < 0.05). In contrast, the effect of GDP per capita remains mixed and statistically insignificant. The Hausman test confirms the superiority of the fixed-effects model over random effects (χ² = 49.53, p < 0.001). Structural break tests using the Bai–Perron method identify two major shifts in 2007 and 2015, while Chow tests indicate that the effect of party control on RPS did not significantly differ before and after 2009 (χ² = 0.93, p = 0.335). These results affirm the policy feedback framework, highlighting that institutional inertia and lagged political influence shape renewable policy outcomes over time. Finally, Bai-Perron multiple structural break test to investigate the temporal variability in the factors influencing Renewable Portfolio Standards (RPS) adoption across U.S. states from 2001 to 2024. The findings of this study contradict the null hypothesis of parameter stability, thereby identifying two statistically significant structural breaks in 2007 and 2015.

Abstract: As Australia advances toward a net zero economy, system-wide transformations in the energy sector are becoming increasingly necessary. This transition entails the electrification of key sectors, the integration of renewable energy sources, and the decommissioning of aging infrastructure. However, alongside technological change, there is a growing need to manage emerging forms of waste such as solar panels and batteries and to embed circular economy principles into the transition framework. This paper presents findings from a qualitative study conducted to understand key stakeholder perspectives on policy coherence between net zero and circular economy policies in Australia. The study reveals that there is significant gap in conceptual understanding of both circular economy and net zero transitions and a lack of clear definitions within these policies leading to two classical systems traps: policy resistance and seeking the wrong goal. The focus on recycling and operational emissions within CE and net zero policies respectively, typically lead to suboptimal outcomes being pursued for both policies. These findings underscore the critical need for capacity building, clearer policy articulation, and targeted educational strategies to foster a socially informed, circular approach to decarbonization. By integrating the clean energy transition within broader social and institutional contexts, this paper contributes to a more inclusive and systemic understanding of Australia's net zero future.

Abstract: Local public workers are central to implementing sustainable development policies at the local level, yet the determinants of their wage growth remain underexplored from a sustainability governance perspective. Building on the “decent work” agenda embedded in SDG 8, this study examines how political context, fiscal capacity, and local wage institutions combine to shape wage increases for local public workers (LPWs) in South Korea. Using fuzzy-set Qualitative Comparative Analysis (fsQCA) on 17 regional governments for 2018–2021, we test whether configurations of progressive local councils, fiscal capacity and autonomy, living-wage adoption, socio-economic context, and workforce composition are sufficient for high LPW wage growth. No single condition is necessary across years; instead, distinct pathways emerge. In 2018, high wage growth is associated with configurations combining progressive councils with larger LPW workforces and supportive socio-economic context. In 2020–2021, fiscal capacity and autonomy become more salient, with high wage growth occurring where stronger fiscal conditions align with either progressive politics or institutional wage standards. The findings highlight that sustainable wage governance is configurational and time-varying, implying that policy mixes should balance decent work, local fiscal sustainability, and equitable service capacity.

Abstract: This article proposes and validates a finance-oriented 5P–ESG composite index to provide an integrated assessment of the sustainable, financial, and corporate governance perfor-mance of firms in emerging markets, with application to the MSCI COLCAP universe. The conceptual framework is derived from the “5Ps” approach of the 2030 Agenda (People, Planet, Prosperity, Peace, and Partnerships), which structures sustainable development goals into five operational, comparable dimensions that are relevant for decision-making in corporate governance and capital market contexts. To operationalize the construct, a set of corporate indicators was defined, data cleansing and standardization procedures were applied to ensure comparability across issuers, and pillar-level scores were constructed. Subsequently, the overall index was estimated through weighted aggregation using en-dogenous weights derived from principal component analysis, following methodological recommendations for composite indices aimed at mitigating collinearity and dou-ble-counting issues. The robustness of the instrument is supported by internal consistency tests and measures of sampling adequacy for factor analysis (KMO/Bartlett), providing evidence of the statistical coherence of the measurement framework. From an applied perspective, the index enables the relative classification of issuers (laggards–transition–leaders) using indicator terciles, offering a quantitative tool for screening, coverage priori-tization, and support for investment and sustainable governance decisions within fun-damental analysis. The findings are interpreted in light of the accumulated evidence on the relationship between ESG practices, financial performance, and cost of capital, high-lighting the usefulness of the approach in emerging markets characterized by heteroge-neous regulatory frameworks and ESG disclosure levels.

Abstract: Financial risk management faces critical challenges in Human Reliability Analysis (HRA) for decisionmaking processes, particularly in quantifying dependencies between sequential decisions and mitigating human error under dynamic market conditions. This paper proposes the AI-Augmented Financial Risk Assessment and Dependency Analysis (A-FRADA) framework, which integrates artificial intelligence with traditional financial risk methodologies to enhance dependency quantification, transparency, and real-time capability. The framework employs a multi-layer architecture combining Bayesian Networks, Gaussian Processes, deep learning, and Large Language Models (LLMs) within an explainable AI (XAI) structure. Through comprehensive architectural diagrams, workflow visualizations, and systematic performance evaluation, we demonstrate and discuss current research that suggest A-FRADA significantly improves accuracy (90-95%), scalability (92-96%), and latency (90-95 ms) while maintaining regulatory compliance through transparent decision-making. Our analysis reveals that the proposed framework not only outperforms traditional and baseline AI methods across all key metrics but also provides a robust, interpretable, and scalable solution for ependency analysis in financial risk chains, supporting both operational resilience and regulatory adherence. This is review paper and all results are from cited literature with focus on graphical and tabular summarization of current landscape.

Abstract: This paper presents a comprehensive policy framework to position New Jersey as a national leader in artificial intelligence (AI) education and workforce development. Through analysis of current state initiatives—including the NJ AI Hub, AI Task Force reports, apprenticeship programs, and regulatory guidance—we identify strategic gaps and opportunities across K-12, higher education, and workforce development sectors. We propose a multi-layered approach visualized through interconnected frame works: an integrated AI education ecosystem, phased implementation roadmaps for K-12 AI literacy, a statewide AI curriculum consortium structure, multi-track workforce development pathways, and equity and access frameworks. Quantitative analysis reveals that while 25%+ of New Jersey’s workforce already uses AI technology daily, only 20-25% of educators feel prepared for AI integration. Our policy recommendations address this gap through a $165 million annual investment strategy with projected 3.8x return on investment, creating pathways for 15,000-20,000 new AI jobs by 2030. This framework provides actionable guidance for lawmakers, educators, and industry stakeholders to enhance New Jersey’s competitiveness, ensure ethical AI deployment, and foster inclusive economic growth in the AI era. Drawing from over recent sources including state publications, academic research, and industry reports, this paper offers concrete ecommendations for lawmakers, regulators, educators, and industry stakeholders to enhance New Jersey’s competitiveness, ensure ethical AI deployment, and foster inclusive economic growth in the AI era. Recommendations include establishing AI literacy standards for all K-12 students, creating specialized AI high schools, expanding community college AI programs, developing industry-aligned university curricula, and implementing statewide AI teacher training. We also address equity considerations, funding mechanisms, and implementation timelines.

Abstract: As the reliability and validity of forensic evidence, particularly in feature comparison disciplines, confront on-going scrutiny, forensic practitioners must ensure their processes, whether for investigative, intelligence or evidential purposes are robust, scientifically grounded, and validated. In forensic facial identification, morphological analysis is internationally recognized as the preferred method for facial image comparison, and is applied during the analysis and comparison steps of the Analysis, Comparison, Evaluation, Verification (ACE-V) process, commonly applied in feature comparison. While several international proficiency tests have assessed forensic facial examiners’ accuracy in comparing mated and non-mated pairs (black box tests), fewer opportunities have focused on evaluating inter-laboratory procedures and methods. To address this gap, members of a small border and immigration focused expert working group participated in an inter-laboratory collaborative exercise designed to analyse and harmonize best practices across member laboratories. There are limited published validation studies of facial image comparison methods. This paper presents the results of a collaborative exercise that compares the methodologies of three different agencies, highlighting key similarities and differences in examiner process and decision making, and provides a foundation for the development of similar future initiatives.

Abstract: This paper conducts a rigorous comparative analysis of U.S. and Chinese strategic frameworks for AI literacy and adoption, with specialized focus on agentic AI systems capable of autonomous reasoning and execution. We systematically examine national policies, educational integration, governance structures, and technological roadmaps, employing both qualitative review and quantitative modeling. Mathematical formulations include multi-dimensional literacy scoring, Bass diffusion models for adoption dynamics, risk assessment functions, regulatory effectiveness indices, competitiveness metrics, and optimization frameworks for resource allocation. Our analysis reveals divergent strategic paradigms: the U.S. favors decentralized, innovation-driven approaches with emphasis on interoperability and public-private collaboration; China pursues centralized, state-led strategies with comprehensive content labeling and rapid systemic integration. We propose a hybrid governance architecture that synthesizes strengths from both models, supported by algorithmic implementations and sensitivity analyses. Drawing from recent publications (2021-2025), we identify critical trends, challenges, and strategic implications. The paper concludes with evidence-based recommendations for policymakers, educators, and industry stakeholders navigating the complex landscape of global AI competition. The paper concludes with actionable recommendations for policymakers, educators, and industry leaders engaged in the global AI race.

Abstract: This article advances EcoTechnoPolitics as a transformational conceptual and policy rec-ommendation framework for hybridizing digital–green twin transitions under conditions of planetary polycrises. It responds to growing concerns that dominant policy approaches by supranational institutions—including the EU, UN, OECD, World Bank Group, WEF, and G20—remain institutionally siloed, technologically reductionist, and insufficiently attentive to ecological constraints. Moving beyond the prevailing digital–green twin transitions paradigm, the article coins EcoTechnoPolitics around three hypotheses: the need for planetary thinking grounded in (i) anticipatory governance, (ii) hybridization, and (iii) a transformational agenda beyond cosmetic digital–green alignment. The research question asks how EcoTechnoPolitics can enable planetary thinking beyond digital–green twin transitions under ecological and technological constraints. Methodologically, the study triangulates (i) an interdisciplinary literature review with (ii) a place-based analysis of two socially cohesive city-regions—the Basque Country and Portland (Oregon)—and (iii) a macro-level policy analysis of supranational digital and green governance frameworks. The results show that, despite planetary rhetoric around sustainability and digitalization, prevailing policy architectures largely externalize ecological costs and consolidate technological power. Building on this analysis, the discussion formulates transformational policy recommendations. The conclusion argues that governing plan-etary-scale ecotechnopolitical systems requires embedding ecological responsibility within technological governance.

Abstract: This paper proposes the Computable Structure of National Narrative (CSNN) framework, treating state-level political texts as engineering-oriented governance systems. Using President Xi Jinping's 2026 New Year Address as a case study, it constructs a multi-level variable and causal pathway model encompassing ‘governance input—transformation mechanism—governance output’. The research integrates computational content analysis, sentiment analysis, and semantic network analysis to transform the text into a reproducible variable system: independent variables encompass development/innovation, people's livelihoods, culture, discipline, and external governance narratives; mediating variables include policy perceptibility, emotional resonance, and governance credibility; dependent variables are governance legitimacy and social cohesion; external uncertainty is introduced as a moderating factor. Results reveal: national narratives exhibit stable functional paragraph sequencing; sentiment is not an end-stage effect of communication but a key mediator in generating governance legitimacy; governance legitimacy displays structural output characteristics, dependent on the convergence of multiple mediating pathways. This study contributes a computable, interpretable, and transferable toolchain for political narrative research, providing a reproducible empirical framework for cross-year, cross-national, and multimodal expansion.

Abstract: In most poor and developing countries, the authorities largely pursue different objectives, which naturally end up diverging in different directions. This divergence arises from the outset due to the inability of these same governments to effectively align these objectives with what they actually want in the short, medium and long term in particular. In this particular approach, I analyse some implications related to the objectives pursued by policy makers in most poor countries. Evidence shows that governments that pursue targeted objectives, such as those related to spending on education, research and development, have been the countries that have managed to break the vicious circles. On the other hand, other factors also contribute to this growth in particular. For example, countries that pursue a non-exclusive democracy aligned with the objectives of the majority tend to differ significantly from governments that pursue democracies aligned with the objectives of a small group. However, in some countries where democracies are in fact at the service of vicious circles, there tends to be strong resistance to how these vicious circles should be broken. democracies are at the service of vicious circles, they tend to show strong resistance to how they should break the vicious circles.