Evaluation Review最新文献

By exploiting variations in Vietnamese districts affected by intense bombings, I establish a causal relationship: residents in heavily bombed areas are more willing to spend on worship practices. This relationship varies among regions, with the primary channel for this effect being the density of graveyards commemorating war martyrs.

Against the backdrop of China's new urbanization and regional coordination strategies, the social integration of rural-to-urban migrants has become an increasingly critical indicator of state governance modernization and institutional performance. Considering rural migrants in Jilin Province as the research subject, particularly in the context of its agricultural revitalization and urbanization policies, this study constructs an "embedding-activation" theoretical framework grounded in institutional theory and social capital theory. It proposes a mechanism in which formal institutions are embedded within informal institutions and generate effective governance outcomes through cognitive transformation facilitated by social capital. Empirically, the study applies structural equation modeling to identify the direct and indirect pathways through which policy cognition, social trust, identity, and economic integration influence social integration. Additionally, machine learning methods are employed to reveal the marginal contributions and nonlinear transition characteristics of individual variables, bridging the gap between causal explanation and predictive accuracy. The results demonstrate that social capital functions as a cognitive trigger for activating institutional efficacy and plays a key mediating role in promoting migrants' social integration. The success of institutional incentives depends on the accumulation of individual cognition and the threshold effects of trust and identity mechanisms, resulting in a behavioral shift from passive acceptance to active engagement. This dynamic promotes the expansion of scope and deepening of structure in social integration. By adopting an institution-cognition-behavior analytical lens, this study enriches the micro-foundations of institutional theory and provides support for enhancing the evaluation, responsiveness, and effectiveness of migration.

University students are increasingly turning to Generative Artificial Intelligence (GenAI) tools for help with their academic assessments, which has prompted major concerns relating to academic integrity. While Universities globally are building guidance on good practice in the use of GenAI, there is a lack of empirical understanding of student perceptions of what ethical and equitable use means to them. Developing insight into student understanding of GenAI is important in enabling institutions to offer more appropriate training and support to encourage good practice in assessment, not just from the perspective of avoiding malpractice, but in identifying ethical opportunities for integrating the technology as a useful tool. Using the GenAI literacy framework as the theoretical foundation, this paper analysed students' GenAI usage in academic assessments within the UK context. Data were collected from 80 participants through focus groups conducted by four UK institutions. Our findings show that students are exploring the potential of GenAI as a tool and are beginning to understand where the ethical boundaries might sit. They are keen to use the technology to support their learning but have significant concerns about locating that boundary between good practice and that which exposes them to accusations of cheating, or which limits their own learning. The paper reinforces the importance of providing GenAI literacy training to university students, so they may develop a better understanding of how GenAI can support learning processes in an ethical way.

The major global challenges of our time-such as climate change, environmental degradation, rising inequality, and the emergence of disruptive technologies-demand interdisciplinary research to generate effective solutions. A clear understanding of value is essential for guiding socio-cultural and economic transitions to address these issues. Despite numerous attempts to define value, existing approaches remain inconsistent across disciplines and lack a comprehensive framework. This paper introduces a novel perspective on value through the lens of granular interaction thinking theory, proposing an informational entropy-based notion of value. Grounded on quantum mechanics, Shannon's information theory, and the mindsponge theory, this framework integrates both subjective and objective considerations and is highly compatible with interdisciplinary research. The informational entropy-based notion of value effectively bridges diverse concepts of value, including use and exchange value in economics, personal values in psychology, and cultural, moral, ethical, and esthetic values in society. By offering a unifying perspective, granular interaction thinking theory provides a valuable framework for translating insights from quantum mechanics into socio-cultural, economic, and psychological contexts, enriching theoretical discourse and enhancing analytical effectiveness.

This paper investigates the systemic risk spillovers from the oil and gold markets to China financial market. Specifically, this paper uses wavelet analysis methods combined with time-varying copula models to calculate the CoVaR to explore the risk tail correlation and the strength of systemic risk spillover effects among oil, gold and China financial market under time-frequency conditions. The empirical results are as follows: First, there is a systemic risk spillover from the oil and gold markets to China financial market with significant time-varying characteristics. Second, changes in oil prices exacerbate risk spillovers from China financial markets, while price volatility in the gold market has a moderating effect on its risk spillovers, with system-wide risk spillovers increasing significantly in times of major economic crisis events. Third, oil market has a greater intensity of systemic risk spillover to China financial market than the gold market. External economic events significantly affect the intensity of risk spillovers from oil to China financial market, and abnormal volatility shocks in internal stock markets significantly affect the intensity of risk spillovers from gold to China financial market more significantly than the impact of external events. Furthermore, in the frequency domain, the oil and gold markets have some lagging effect on the transmission of systemic risks to the China financial market.

This article reflects on how the Independent Evaluation Group (IEG) of the World Bank Group grapples with the issue of generalizability in conducting large-scale and complex development evaluations. It discusses a practical framework IEG uses to meet three needs: methodological, institutional, and didactic. IEG evaluations are aimed at informing significant organizational or strategic decisions encompassing broad scopes, assessing extensive portfolios of interventions by the organization across diverse contexts and spanning multiple years. These evaluations are inherently multimethod and need to bridge various logics of generalization. They seek to influence decision-makers, such as boards of directors or senior managerial teams, with the objective of guiding pivotal moments in the organization's trajectory; ensuring accountability for learning, results, or budget expenditures; and synthesizing substantial evidence to distill key success or failure factors for future strategic planning. The defensibility of the methodological scaffolding is paramount to the credibility of the evaluations. The article discusses the challenges inherent in such evaluations, including the need to generate findings that are valid at multiple levels of analysis and the reliance on multitiered mixed-methods approaches. It examines the use of a practical framework to bridge methodological principles and real-world challenges involved in evaluation to inform the design and implementation of large-scale evaluations. The framework is illustrated with examples from IEG's evaluations, and the article explores how practitioners and researchers can apply the framework in other settings to enhance the generalizability of their findings.

The relationship between climate risks and commodity markets remains insufficiently explored, especially when analyzed through the lens of high-frequency data. This study seeks to address this gap by investigating the spillover effects of global climate risks, both physical and transitional, on key commodity markets and employs a novel analytical framework. By utilizing newly developed climate risk indices alongside the innovative mixed-frequency spillover measure, this research combines high-frequency climate risk data with the responses of low-frequency commodity prices. Our results highlight notable spillover effects, demonstrating that climate risks serve as the primary drivers of spillovers to commodity markets in a mixed-frequency data context, whereas such effects are not observed within a common-frequency data environment. These findings have important implications for policy-makers and investors, indicating that current market analyses may not capture the influence of climate risk adequately.

I explore how "low-quality evidence" from program performance might still be useful in decision-making. Conceptually, a local government named "Here" is motivated to consider a program from "Elsewhere" that seems to show year-over-year exemplary performance. Here must manage five sources of uncertainty about whether and how to extrapolate from Elsewhere: chance in assessing Elsewhere's performance; illusion due to confounding variables; estimating the several powers of the program's components; substitutions in the design process made by Elsewhere and contemplated by Here; and estimating whether in the final analysis Here can meet its own breakeven criterion for going ahead. Here can begin with Elsewhere's experience, but it still must do much thinking and information-collecting on its own.

Gamified Flipped Classrooms (GFC) are increasingly implemented in teaching and have become a trending research subject. Gamification and Flipped Classroom (FC) literature expresses a clear need for a theoretical foundation that positively affects motivation, behavioral change, and learning. However, this requires an overview of the current theoretical foundations of GFC research. Therefore, this study conducted a systematic mixed studies review (SMSR) of the current theoretical foundations of GFC research and learning effects. Sixty-nine studies were identified after applying rigorously defined inclusion and exclusion criteria. The review revealed that a significant number of studies (25 of 69) lacked a theoretical foundation. In studies supported by theory, Self-Determination Theory (SDT) was most frequently employed. Moreover, only three studies used theories to design both gamification and FC components. While GFC research generally shows positive effects on learning, studies with a theoretical background reported a higher rate of positive GFC outcomes (56%), compared to only 29% for studies lacking a theoretical foundation. Future research should explore and attempt to link SDT and other theories to learning impacts.

This study introduces a novel aligned technical index, derived from multiple technical indicators, that encompasses a broader spectrum of technical measurement strategies than those obtained from previous 3PRF (Three-Pass Regression Filter) research. Our empirical results demonstrate that this index exhibits significant predictive power for new energy price returns in both in-sample and out-of-sample tests. This index is extracted using the 3PRF method and yields significantly better results than those obtained with traditional methods. Considering that the market typically operates in two states, we incorporate a regime-switching model with time-varying transition probabilities into our forecasting framework. The findings indicate that the technical index influences the probability of regime transitions between states and that the inclusion of a regime-switching model further enhances predictive performance. The incorporation of the regime-switching mechanism further improves the predictive performance of the model. Moreover, from an asset allocation perspective, both the technical index and regime-switching models deliver considerable economic value to mean-variance investors.