ABSTRACT The Common Agricultural Policy (CAP) has become one of the EUs most contested policy areas. But despite widely observed patterns of rural resentment and farmers’ resistance to environmental policies, few studies have examined the CAPs political consequences beyond recent farmers protests. Drawing on policy feedback theory, Bourdieu’s theory of symbolic fields, and scholarship on sociotechnical imaginaries, this paper offers an interpretive policy analysis based on interviews, participatory observation, and digital ethnography in Sweden and Italy – two contrasting contexts of EU agricultural governance. The study introduces the concept ‘symbolic stratification’ to explain patterns in farmers’ reactions to the EUs promotion of an entrepreneurial farmer ideal. It offers a typology of four different farmer identities – entrepreneurial, traditional, environmentally caring, and resentful – the relative standing of which is significantly affected by EU policies. The findings have bearing on the EUs legitimacy in rural areas and Europe’s capacity to transition to sustainable food production.

BackgroundIn response to high rates of undernutrition, Uganda began rolling out the multisectoral Uganda Nutrition Action Plan (UNAP) in 2011, followed by Phase II (UNAP II) in 2020. Uganda has since progressed toward several nutrition targets; however, limitations in nutrition coordination and financing call for continued strengthening of the UNAP II.ObjectivesGiven that monitoring of the UNAP II does not consistently capture district-level outcomes and relies upon quantitative nutrition indicators, this study aims to qualitatively assess nutrition stakeholder and community perspectives at the district level.MethodsFrom June to August in 2022, under an umbrella study utilizing community-based participatory research methods to train youth from Lira District in research, in partnership with the University of Southern California (USC) and community organization Children's Chance International-Uganda, these youth helped carry out an explanatory qualitative assessment consisting of 10 key informant interviews. Responses were compiled, transcribed, and analyzed using the Atlas TI software to code responses for thematic insights.ResultsParticipants highlighted several challenges with the UNAP II implementation at the local level. Mechanisms intended to promote multisectoral collaboration, such as district nutrition coordination committees, have been on hiatus due to limited funding; local governments experience funding disbursement delays and a lack of nutrition prioritization; and nutrition surveillance is limited by tools, training, and human resources.ConclusionsObtaining community perspectives revealed several gaps in local UNAP II implementation and demonstrates the importance of creating mechanisms for communities to continuously provide feedback for national policies like the UNAP II.

Purpose This paper aims to examine how strategic autonomy in semiconductor policy can lead to governance overreach. It argues that excessive state intervention – justified by security and resilience narratives – may reduce flexibility and innovation. To address this, the study develops a Policy Overreach Governance Framework (POGF) to analyze how autonomy discourses reshape institutional dynamics and create self-reinforcing governance traps in global semiconductor governance. Design/methodology/approach Using a qualitative comparative approach, this paper integrates securitization theory, policy feedback and adaptive governance. The POGF identifies four dimensions – institutional expansion, narrative entrenchment, coordination erosion and feedback deficit – and is applied to five jurisdictions: the USA, European Union, Japan, South Korea and Taiwan. Analysis is based on policy documents, reports and scholarly literature. Findings Across all cases, security-oriented industrial policies expand state intervention and entrench policy narratives. These dynamics weaken coordination and reduce feedback, creating governance traps that limit innovation and collaboration. While intended to enhance resilience, strategic autonomy often becomes ideological and counterproductive. The study highlights the need for evaluation, coordination and balanced narratives to maintain adaptability. Originality/value This paper introduces the POGF to examine the institutional risks of strategic autonomy. By linking securitization, feedback and adaptive governance theories, it shifts focus from competitiveness to governance flexibility. The framework provides an early-warning tool for policymakers to detect overreach and recalibrate semiconductor strategies before resilience efforts become rigid or self-defeating.

Context: Target populations do not always recognize policy benefits. This may be particularly true when policy design, informational environment, or political conflict make a policy's benefits difficult to discern, which is the case for the Affordable Care Act (ACA). Although many groups benefited from the ACA, the attitudes of seniors—one important target population of the ACA—remain unexplored. Methods: A survey of 1,206 Americans age 65 and older was fielded in the summer of 2021 to assess the effect of three informational treatments about the ACA's benefits—extending the life of the Medicare trust fund, filling the Medicare Part D donut hole, and reducing the number of uninsured people—on the ACA's favorability and on attitudes about its future and about party leadership in the domain of health care. Findings: Priming individuals about the ACA's benefits improved its favorability, particularly for subgroups generally opposed to the ACA, such as Republicans and those high in racial resentment. Attitude changes about the future of the ACA were limited to benefits directly focused on seniors. There were no broader spillover effects on attitudes about partisan health care leadership. Conclusions: The findings have implications for research on the ACA, policy feedback effects, self-interest, and priming effects.

We investigate policy actors’ experiences of the implementation of state takeover policy in three primarily Black school districts in St. Louis, MO using a systems theory approach. Authors conducted qualitative interviews with policy actors from three St. Louis districts and other select districts across the nation who experienced a state appointed governance intervention. Qualitative thematic analysis was used to identify major themes. Three major themes emerged across 18 respondents: state-community divide, community suffering, and oppressive mechanisms. Results suggest an environment that may contribute to or create experiences of trauma. The implementation of state takeover policy intersects with heightened emotionality among many community members in primarily Black school districts. Health and trauma are rarely considered as educational policy feedback in the literature, particularly in discussions around state takeover. Future research must consider psychological implications of policy in historically marginalized districts and wider communities.

Digital governance is often expected to expand public participation, strengthen transparency, and foster deliberative democracy by enabling dialogic interaction between the state and citizens. Yet, whether digital government platforms can facilitate communicative action and equal deliberation remains contested, given risks of fragmentation, algorithmic influence, and unequal access. This study critically assesses the extent to which Habermasian deliberative democracy is realized in contemporary digital governance practices, examining whether government platforms function as arenas for rational deliberation or reproduce new forms of exclusion and social isolation. Using a qualitative approach based on critical literature review and conceptual analysis of policies, platform designs, and online participation practices, the study finds that while digital platforms open channels for two-way communication, participation is largely procedural, elitist, and fragmented. Digital spaces frequently operate as sites of symbolic expression rather than substantive deliberation, shaped by technocratic rationality and structural power. The study argues that strengthening deliberative democracy requires participatory platform architecture, transparent policy feedback, and efforts to reduce algorithmic domination and digital inequality.

This comparative study of Virginia and Florida (2000–2025) examines how contrasting state governance structures shape local digital transformation. Findings from Digital Counties and Digital States Survey rankings, practitioner interviews, and policy analysis show that Virginia’s centralized Dillon’s Rule framework, supported by sustained state funding, produced equitable digital capacity across urban and rural counties. Florida’s traditionally decentralized Home Rule approach fostered urban innovation but constrained rural diffusion—a limitation its new broadband trust fund now addresses. Integrating multi-level governance and policy feedback perspectives, the study proposes a sequenced hybrid heuristic: states first mandate affordability and build foundational infrastructure to close persistent rural employment and tradable-sector gaps, then empower local collaborative networks to drive inclusive innovation and economic revitalization. This framework transcends the centralization–decentralization dichotomy and offers federated systems a replicable pathway from infrastructure deployment to equitable rural prosperity.

Medicare risk adjustment is a cornerstone of payment determination in Medicare Advantage, yet existing hierarchical condition category–based models face increasing limitations related to predictive accuracy, equity, and responsiveness to evolving healthcare delivery patterns. Traditional approaches rely primarily on retrospective claims data and linear modeling assumptions, constraining their ability to capture nonlinear relationships, temporal dynamics, and emerging risk signals. Advances in artificial intelligence (AI) and machine learning present opportunities to modernize Medicare risk score forecasting by improving precision and adaptability; however, their application within a federally regulated payment system introduces significant methodological, ethical, and policy challenges. This review synthesizes the literature on AI-driven forecasting methods relevant to Medicare risk adjustment, critically examining their performance advantages, risks of algorithmic bias, transparency and explainability constraints, coding incentives, and regulatory implications. Building on this synthesis, the article proposes a structured conceptual framework for AI-driven Medicare risk score modernization that integrates advanced predictive modeling with governance, fairness evaluation, auditability, and policy feedback mechanisms. The review concludes that hybrid, governance-aware AI approaches may enhance risk score accuracy while preserving equity, transparency, and accountability, offering a pragmatic pathway for responsible modernization of Medicare risk adjustment in an increasingly data-driven healthcare system.

When negative feedback becomes self-undermining: instrument termination in environmental regulation

ABSTRACT Since 2008, EU governments repeatedly pooled their means of fighting severe crises. This happened in an environment where EU policy-making became more politicised, above all polarising and salient. Politicisation is widely considered to constrain EU decision-making. We study how this constraint was overcome by crisis policy feedback. Our case is sovereign bailout funding, which in the 2010s had politicised EU fiscal governance as never before, raising high hurdles for further fiscal institution-building. Yet, the Recovery and Resilience Facility (RRF), introduced in 2020-2021, amounted to pre-emptive bailout funding that was publicly and controversially discussed. Based on policy process tracking data, we argue that elites strategically politicised the economic response to the Covid-19 pandemic by critically referencing the policy legacy of the European Stability Mechanism in the Euro Area crisis. Our analysis thus provides evidence for negative policy feedback, driven by top-down politicisation. The wider significance of our finding is that politicisation may no longer be a constraint on European integration but facilitating contested polity formation.

District housing policies and information networks are major factors in school choice in the United States. This article unpacks the interaction between the two in the context of Chinese families making mobility decisions for their primary school–age children. Using data from twenty semi-structured interviews with Chinese families and qualitative content analysis of WeChat groups and online forums in Northern Virginia and theorizing with Bourdieu’s capital, Coleman’s social capital, and policy feedback, the analysis illustrates the role of district housing policies in limiting school access in practice and online Chinese parent communities’ parallel infrastructure for information and social capital. The article reveals that families with more developed digital literacy and denser social capital are more successful at translating information to policy reading and school choice, and some others are constrained by district housing and information access. The article, by bringing housing policy and digital social capital to the study of school choice, contributes to both comparative education and cultural sociology.

Abstract While scholars have documented feedback effects among a policy's direct winners and losers, less is known about whether such effects can occur among the indirectly affected—“the policy adjacent.” Using 458 geocoded housing developments built between two nearly identical statewide ballot propositions funding affordable housing in California, we show that policy generates feedback effects among neighboring residents in systematic ways. New, nearby affordable housing causes majority‐homeowner blocks to increase their support for the housing bond, while majority‐renter blocks decrease or do not change their support. We attribute the positive effect among majority‐homeowner blocks to the housing's replacement of blight. In contrast, the lack of a positive effect among majority‐renter blocks may be driven by the threat of gentrification. Policy implementation can win support for expansion among unexpected beneficiaries, while failing to do so even among the policy's presumed allies.

Abstract The uncertainties associated with renewable energy generation and the challenges in the precise modeling of microgrids present significant difficulties for conventional planning algorithms. Owing to its model-free nature, strong hyperparameter robustness, and broad applicability, the Proximal Policy Optimization (PPO) algorithm, which is based on the Actor-Critic architecture, shows considerable promise as an ideal solution for the optimal scheduling of microgrids. However, in the standard PPO training process, the update of the value network lags behind that of the policy network, which compromises training efficiency. Moreover, the inherent randomness in agent exploration can lead to instability in policy updates. To address these limitations, this study proposes an enhanced PPO algorithm that incorporates a policy feedback mechanism and employs a segmented clipping mechanism. These improvements significantly accelerate the overall training convergence speed and effectively mitigate severe fluctuations during training, thereby enhancing the stability of the network updates. Experimental results in a microgrid energy management scenario demonstrate that the proposed algorithm achieves faster convergence and greater stability during the training phase. During the testing phase, it is capable of generating high-quality intra-day scheduling strategies based on real-time electricity consumption conditions. Over a testing period of ten days, the proposed algorithm achieved a 19.87% reduction in cumulative electricity costs and a 20.25% decrease in cumulative power imbalance compared to the standard PPO. The proposed algorithm outperforms the existing PPO method, offering an effective solution for microgrid energy management and demonstrating substantial application value.

Artificial Intelligence (AI) is rapidly becoming a cornerstone technology for achieving ecological sustainability. From optimizing energy systems and reducing agricultural waste to monitoring biodiversity and predicting climate risks, AI enables data-driven decision making that can substantially reduce environmental footprints. This paper reviews AI methods relevant to eco-sustainability, proposes a modular framework — EcoAINet — for integrating sensing, modeling, optimization, and policy feedback, and describes experimental designs and evaluation metrics across three applied domains: (1) renewable energy optimization, (2) precision agriculture and food-waste reduction, and (3) biodiversity monitoring. We present recommended architectures, loss/objective formulations, datasets, baselines, and ethical/regulatory considerations. The work is written to be immediately usable as a seminar paper and a blueprint for follow-on research.

This article develops an integrated theoretical-analytical framework to understand institutional innovation and policy learning in biofuel governance within energy transition contexts. Through comparative institutional analysis of Brazil, the European Union, the United States, and India, we examine three core questions: how policy design generates political-institutional feedback, how learning processes mediate regulatory change, and how adaptive governance affects regulatory effectiveness. The framework articulates four causal mechanisms: political-institutional feedback, policy learning, innovation system dynamics, and regulatory credibility effects. The analysis draws on legislative documents, implementation reports, and stakeholder interviews across four jurisdictions spanning 1975–2025. Evidence demonstrates that blending mandates combined with market instruments enhance biofuel consumption but depend critically on three conditions: life-cycle assessment consistency, certification market governance, and regulatory predictability. Excessive administrative flexibility generates negative feedback that undermines credibility, while effective policy learning requires institutional entrepreneurs mediating between technical knowledge and political coalitions. Differences in administrative capacity, state intervention traditions, and actor networks explain effectiveness variations across nominally similar instruments. These mechanisms reveal why identical policy designs produce divergent outcomes. The study contributes by integrating policy feedback theory, learning-in-governance frameworks, and innovation systems perspectives into a multilevel model connecting short-term implementation with long-term institutional trajectories. It specifies mediating and moderating variables conditioning instrument effectiveness, challenging linear policy transfer assumptions. Policy implications indicate that regulatory effectiveness depends on institutional preconditions rather than instrument choice alone; successful transitions require coordinated capacity-building alongside policy adoption.

ABSTRACT Why do neoliberal policies endure despite their contentious record? One school of thought emphasises the power that corporate actors have amassed as markets have been globalised and financialised. This article concurs but departs from the tendency to see business support as monolithic. Across a wide range of policy domains it highlights the potential for inter-firm conflict – particularly between larger, more internationalised companies and those likely to feel the pressure of foreign competition most intensely. The paper argues that liberalising reforms alter corporate demographics in ways that strengthen neoliberalism itself. Larger, internationally integrated firms – most likely to endorse market opening – grow their ‘structural prominence’, capturing a greater share of sales and profits. Meanwhile, smaller or less globally integrated enterprises often succumb, exiting the market. Drawing on multi-decade, cross-national data, it shows a marked upswing in inter-firm inequality. Using an instrumental-variable strategy, it further demonstrate that neoliberal reforms account for much of this skewing of corporate power.

Traditional teaching evaluation methods are often retrospective and coarse-grained, lacking the continuous, moment-to-moment feedback required for real-time pedagogical adaptation in language training. Existing intelligent tutoring systems frequently fail to address this, underutilizing multimodal behavioral signals and leaving a critical gap in understanding whether such signals are essential for learning effective policies. This paper introduces a Reinforcement Learning (RL) framework to solve this problem. We propose a hybrid cognitive-linguistic model using a Proximal Policy Optimization (PPO) actor-critic agent, which operates on a novel 516-dimensional state vector that fuses a 512-dimensional semantic embedding from a pre-trained T5 model with a 4-dimensional vector of simulated cognitive-behavioral signals (correctness, response time, attention, hint request). Tested in a simulated learner environment built on the Tatoeba corpus, our agent autonomously discovers a highly effective policy, achieving a mean episodic reward of 6.563, on par with the optimal heuristic baseline. The identified policy, albeit optimal in the simulation, embodies a counterintuitive method that significantly prioritized task repetition by the learner. Our hypothesis is confirmed by a critical ablation experiment: an agent that is deprived of the cognitive-behavioral signals does not learn, and they are as good as a random baseline (mean reward 5.213). The present work gives conclusive evidence that multimodal cognitive-behavioral cues are not only supplementary but are an inevitable part of learning by adaptive pedagogical agents. We mainly provide validation of a hybrid state representation that allows an RL agent to learn effective teaching strategies, making way for more useful and customized educational technologies.

Abstract Objective This study examines how social-psychological mechanisms optimize carbon sink monitoring technologies and enhance community participation in conserving Sanya’s mangrove ecosystems. Grounded in environmental psychology and behavioral science, it identifies strategies to improve both technological effectiveness and local engagement in blue carbon initiatives while assessing mental health co-benefits including reduced eco-anxiety and enhanced well-being. Subjects and Methods A mixed-methods approach was employed, comprising quantitative surveys with 300 local residents, fishers, and tourism stakeholders to assess environmental attitudes and willingness to participate; experimental trials with 50 participants comparing traditional expert-led monitoring and AI-assisted community-co-designed monitoring incorporating behavioral nudges; and qualitative interviews with 20 key informants exploring psychosocial barriers and motivational drivers. Data were analyzed using structural equation modeling. Results Psychological engagement significantly improved with community-co-designed AI tools, increasing participation by 45% compared to traditional methods. Collective efficacy emerged as the strongest predictor of sustained involvement. Mental health benefits were observed, with participants reporting lower eco-anxiety and higher well-being. Primary barriers included technological distrust among older residents and perceived inefficacy related to slow policy feedback. Conclusions Social-psychological mechanisms are critical for optimizing carbon sink technologies and fostering community-driven mangrove protection. Integrating behavioral science through personalized feedback, identity-based messaging, and participatory AI enhances both ecological outcomes and resident well-being. Future efforts should address intergenerational engagement strategies and policy frameworks that reinforce psychological incentives. Acknowledgments The research was supported by: (1) the Hainan Provincial Joint Project of Sanya Yazhou Bay Science and Technology City, Grant No: 2021JJLH0055; (2) the Project of Sanya Yazhou Bay Science and Technology City, Grant No: SKJC-JYRC-2024-41; (3) the Youth Project of Yazhou Bay Innovation Institute of Hainan Tropical Ocean University, Grant No: 2022CXYQNXM02; (4) the Hainan Tropical Ocean University Talent Recruitment Scientific Research Startup Project, Grant No: RHDRC202207.

IntroductionHuman-animal relationships have changed significantly in recent decades, becoming increasingly diverse and ethically complex, thereby prompting increased societal concern for animal welfare. This study investigates public perceptions of animal welfare levels and related policies in South Korea, as well as the psychological and contextual determinants of pro-animal behavior as animal welfare engagement, employing the Theory of Planned Behavior (TPB) as its theoretical framework.MethodsA nationally representative online survey was conducted with 2,000 South Korean adults. Measures included attitudes toward animals, subjective norms, internal and external efficacy, behavioral intentions, and self-reported pro-animal behaviors. Structural equation modeling (SEM) was employed to test hypothesized relationships among TPB constructs and behavioral outcomes.ResultsThe findings indicate strong public demand for appropriate and effective political action on animal welfare issues. SEM results show that both pro-animal attitudes and internal efficacy significantly predict behavioral intentions, whereas subjective norms and external efficacy do not exhibit significant effects. Internal efficacy demonstrates both direct and indirect positive influences on pro-animal behavior. In contrast, external efficacy shows no statistically significant direct impact.DiscussionPublic concern for animal welfare in South Korea is increasing, and internal efficacy and pro-animal attitudes play crucial roles in promoting behavioral engagement in animal welfare. Although external efficacy and subjective norms show limited influence, this does not imply that legislative efforts lack value. Rather, institutional support may enhance pro-animal behavior indirectly by strengthening individual confidence, underlining a potential mediating role of internal efficacy between external efficacy and behavioral outcomes. Findings emphasize the need for policies and educational initiatives that enhance individual confidence and motivation while complementing broader institutional frameworks. Future research should incorporate policy feedback theory to better understand the interaction between institutional context and public behavior.

Against the global rollout of Carbon Border Adjustment Mechanisms (CBAMs), carbon tariffs have emerged as a core tool for developed economies to internalize environmental externalities—especially for energy-intensive imports that dominate cross-border carbon flows. However, emission information asymmetry, a critical barrier to implementing cross-border energy and environmental policies, undermines the design of optimal carbon tariffs, as it distorts the link between tariff levels and actual fossil energy-related emissions. This study develops a two-country analytical model to examine how biased assessments of exporters’ carbon intensity influence optimal tariff settings, exporters’ strategic behavior, and aggregate carbon emissions—with a focus on energy-intensive production contexts. The results show that underestimating carbon intensity reduces exporters’ compliance costs, incentivizing emission concealment; this weakens tariffs’ environmental stringency and may raise global emissions. Overestimation, by contrast, inflates exporters’ marginal costs, discouraging green investment and causing emission displacement rather than reduction. The analysis highlights a policy feedback loop wherein misjudged emission information distorts both trade competitiveness and environmental performance. This study concludes that a transparent, accurate, and internationally verifiable carbon accounting system is essential: it not only facilitates the effective implementation of CBAM but also aligns optimal carbon tariffs with CBAM’s dual goals of climate action and trade equity, while supporting global energy transition efforts.