This study investigates how U.S. Federal Reserve interest rate cuts during the 2019–2020 easing cycle influenced the performance of equity mutual funds, with a particular emphasis on contrast between growth and value investment styles. Using an event study framework, we examine abnormal returns, cumulative abnormal returns, and risk-adjusted performance metrics, including those based on both 30 days static and rolling Jensen’s alpha and Sharpe ratios, across short-term (30-day) and long-term (6-month and 1-year) windows surrounding three major rate cut events. Our empirical results show that growth funds significantly outperform value funds following rate reductions, especially over longer horizons. This performance advantage is more pronounced in risk-adjusted measures and strengthens when incorporating rolling dynamics, indicating that and asymmetric sensitivity of fund styles to interest rate changes, shaped by differences in duration exposure and investor sentiment. Overall, this study offers novel insights into how monetary policy influences fund-level dynamics beyond broad market movements and deepens the understanding of monetary transmission in asset management by incorporating time-varying performance metrics.

Purpose: Peer review shapes not only the evaluation of scientific claims but also whose work gains visibility and influence. A growing body of evidence shows that review outcomes can mirror structural inequities tied to authors' identities, language backgrounds, institutional positions, and geographic locations. Most of the existing findings come from observational submission data, which can document disparities without revealing which forms of bias are actually detectable under the noise and constraints of real review systems. This raises an equity concern: some inequities may persist not because they are small but because standard analytical approaches lack the statistical power to uncover them. Design/methodology/approach: This study introduces a stylized simulation of the peer-review process that includes field status, topical fit, multidimensional quality signals, and socially patterned bias attributes such as institutional prestige, Global South affiliation, past co-authorship, and language-related disadvantage. The framework is paired with Monte Carlo power analysis to examine which structural disparities can be empirically recovered under realistic levels of reviewer disagreement, correlated signals, and assignment variability. Findings: The results show that reviewer noise and structural correlation sharply constrain detectability. We found that with sample sizes around 200, we recover only moderate-to-large effects consistently, while smaller or more relational disadvantages remain difficult to distinguish from noise. Consequently, the absence of significant disparities in observational audits cannot be interpreted as evidence of equitable review. Originality/value: By delineating the boundary between detectable and latent inequities, the study offers practical guidance for designing more informative fairness audits and more accountable peer-review systems.

This study investigates how U.S. Federal Reserve interest rate cuts during the 2019–2020 easing cycle influenced the performance of equity mutual funds, with a focus on con-trasting growth and value strategies. Using an event study framework, we examine abnormal returns (AR), cumulative abnormal returns (CAR), and risk-adjusted perfor-mance measured by both static and rolling (30-day) Jensen’s alpha () and Sharpe ratios (SR) across short-term (30-day) and long-term (6-month and 1-year) windows surrounding three major rate cut events. Further statistical tests reveal that growth funds significantly outperform value funds following rate reductions, especially over longer horizons. This performance premium is more pronounced in risk-adjusted returns and becomes stronger when accounting for rolling dynamics, indicating that growth funds are more responsive and sensitive to monetary easing. These findings underscore a persistent and asymmetric sensitivity of different fund styles to interest rate changes, shaped by differences in duration exposure and investor sentiment. This study offers novel insights into how monetary policy influences fund-level dynamics beyond broad market movements and deepens the understanding of monetary trans-mission in asset management by incorporating time-varying performance metrics.

Universities increasingly use large-scale educational data to examine how academic programs communicate curricular priorities and prepare students for emerging computing- and data-intensive fields. Yet limited evidence exists on how documented curriculum signals can be systematically compared with occupation-specific work activities at the program level. This study develops a curriculum analytics framework that represents syllabus-derived curriculum signals from the Course-Skill Atlas and task-derived occupational requirements from O*NET in a shared Detailed Work Activity (DWA) space. The focal analysis examines Computer Science curricula in relation to the O*NET Data Scientist occupation profile. We construct L2-normalized DWA vectors for curriculum groups and occupations, align them by official DWA IDs rather than direct text matching, and evaluate correspondence using cosine similarity, Coverage@K, and a Skill Gap Index. The focal comparison shows full nominal coverage but weak relative-emphasis alignment: all task-derived Data Scientist DWAs appear in the Computer Science curriculum vector, yet they receive relatively low curriculum-side weights. This pattern suggests an emphasis gap rather than an absence gap in documented curriculum signals. To examine the framework beyond the focal case, we use an 8 × 8 curriculum–occupation validation matrix, rank-based positive-control checks, sensitivity analyses, and Core/Applied cohort robustness checks. These analyses indicate that the framework recovers plausible curriculum–occupation patterns and remains stable under alternative coverage and weighting specifications. The study contributes a traceable shared-ontology approach for program-level curriculum analytics, curriculum review, and evidence-informed communication in higher education. We emphasize that DWA-ID alignment should not be interpreted as a direct measure of teaching quality, student learning, or occupational readiness.