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Abstract The importance of creating diverse and equitable environments in higher education has gained growing recognition in recent years (Allen, 2005). While individual-level bias training has shown limited efficacy, this study proposes that program structure—characterized by clear, transparent, and uniformly applied standards, expectations, and norms—may be a more effective route to equity. Leveraging data from a large U.S. public university, we present evidence from multi-level modeling that demonstrates the positive relationship between program structure and equity-related outcomes, including psychological well-being and academic performance. Notably, these effects appear to disproportionately benefit women and underrepresented minority students, suggesting that structure may be particularly impactful for marginalized students, who are often excluded from informal informational networks within their departments. This research contributes to the ongoing dialogue on practical strategies for achieving equity in higher education, offering an alternative to individual-focused interventions. We discuss the theoretical implications for research on marginalized groups and provide actionable recommendations for practitioners. The study highlights the potential of structural approaches in fostering more equitable and inclusive learning environments in higher education. 

A replication package for the article The impact of genetically modified crops on bird diversity. 

Mission-time Linear Temporal Logic (MLTL) represents the most practical fragment of Metric Temporal Logic; MLTL resembles the popular logic Linear Temporal Logic (LTL) with finite closed-interval integer bounds on the temporal operators. Increasingly, many tools reason over MLTL specifications, yet these tools are useful only when system designers can validate the input specifications. We design an automated characterization of the structure of the computations that satisfy a given MLTL formula using regular expressions. We prove soundness and completeness of our structure. We also give an algorithm for automated MLTL formula validation and analyze its complexity both theoretically and experimentally. Additionally, we generate a test suite using control flow diagrams to robustly test our implementation and release an open-source tool with a user-friendly graphical interface. The result of our contributions are improvements to existing algorithms for MLTL analysis, and are applicable to many other tools for automated, efficient MLTL formula validation. Our updated tool may be found at https://temporallogic.org/research/WEST. 

Native bee species in the United States provide invaluable pollination services. Concerns about native bee declines are growing, and there are calls for a national monitoring program. Documenting species ranges at ecologically meaningful scales through coverage completeness analysis is a fundamental step to track bees from species to communities. It may take decades before all existing bee specimens are digitized, so projections are needed now to focus future research and management efforts. From 1.923 million records, we created range maps for nearly 88% (3158 species) of bee species in the contiguous United States, provided the first analysis of inventory completeness for digitized specimens of a major insect clade, and perhaps most important, estimated spatial completeness accounting for all known bee specimens in USA collections, including undigitized bee specimens. Completeness analyses were very low (3–37%) across four examined spatial resolutions when using the currently available bee specimen records. Adding a subset of observations from community science data sources did not significantly increase completeness, and adding a projected 4.7 million undigitized specimens increased completeness by only an additional 12–13%. Assessments of data, including projected specimen records, indicate persistent taxonomic and geographic deficiencies. In conjunction with expedited digitization, new inventories that integrate community science data with specimen‐based documentation will be required to close these gaps. A combined effort involving both strategic inventories and accelerated digitization campaigns is needed for a more complete understanding of USA bee distributions.