American Association For The Advancement of Science

Scientific research underpins effective conservation policy, yet current approaches for assessing whether scientific outputs meaningfully support defined management objectives rely primarily on manual expert review. This limitation constrains scalability, is time intensive and introduces potential bias in identifying knowledge gaps. We present a framework combining AI-assisted multi-target classification with systematic coverage analysis for automated evaluation of research alignment with conservation objectives. We compare traditional machine learning (TF-IDF + logistic regression), a generic BERT baseline, and an enhanced SciBERT approach incorporating domain-specific adaptations including multi-target architecture, balanced loss functions, and target weighting optimized for conservation science. The framework classifies research topics and conservation objective alignment, two dimensions requiring comprehension of scientific content and policy implications. We demonstrate the approach using 295 expert-annotated peer-reviewed studies from the Ross Sea region Marine Protected Area in Antarctica. Our enhanced multi-target SciBERT model achieved 70.0% macro F1, outperforming TF-IDF (59.5%) and BERT (52.0%) baselines, with per-target improvements of 21% on research topics and 14.5% on conservation objectives. The framework achieved 78% agreement with expert annotations, with particularly strong performance on conservation objective alignment (87.7% F1, 94% agreement). The integrated system successfully identified and quantified descriptive patterns in research coverage across thematic and policy dimensions, enabling systematic assessment for research prioritization and automated coverage analysis. While demonstrated in the Antarctic context, the framework architecture is broadly transferable, though successful adaptation requires retraining with domain-specific expert annotations and fine-tuning to match local management frameworks. • Enhanced multi-target SciBERT achieves 78% agreement with expert annotations for automated research-policy alignment assessment. • Framework reveals research coverage patterns with 59% of conservation topics appearing in <15 papers. • Multi-target architecture simultaneously classifies research themes and conservation objectives with 17.6% improvement over TF-IDF baseline. • Demonstrates scalable deployment across diverse contexts from Marine Protected Areas to SDG assessment. • Open-source implementation enables immediate adoption for evidence-based conservation planning.

Abstract The twenty-first-century economy has created an increasing demand for STEM education to prepare students for STEM careers. An emerging example is selective STEM high schools – institutions primarily focused on recruiting high-achieving students interested in pursuing and persisting in STEM fields. While theories of identity development have helped explain how students identify with STEM and how institutional factors relate to this process, further analysis is needed to understand the different contexts within which students who identify with and persist in STEM operate. This study analyzes how twenty-eight students who applied to selective STEM high schools conceptualized and identified with STEM in their first year of postsecondary education in the United States. Interviews were conducted with students who attended selective STEM-focused high schools and those who did not attend or had discontinued attending such schools. Using a phenomenological approach, we examined how students’ self-reported high school experiences relate to their conceptualization and perceptions of interest, recognition, participation, and competence in what it means to be a STEM person. Findings suggest that selective STEM-focused high school experiences gave students a more nuanced understanding of STEM fields. However, students also shared perceptions of incongruence with their interests perceived as unrelated to STEM, continuous stress and mental overload, and antipathy toward mathematics.

Science is one of the greatest engines of health, prosperity, and security across the world. Yet, in the United States, the enterprise is now under tremendous stress from an array of pressures, including threats to federal support for both the direct and indirect costs of research. But funding instability is not the only issue. One major problem is that regulatory and policy requirements force researchers to spend nearly half of their research time on paperwork associated with receiving federal grants and contracts, not on discovery. The situation is made worse when research institutions themselves layer on additional requirements to ensure compliance. The administrative tasks are unnecessarily complex, duplicative, and, in some cases, contradictory. They also waste taxpayer dollars intended for scientific discovery and innovation. Given the current political focus on streamlining federal regulations, there is a clear opportunity to finally solve this burdensome problem.

The Patient Reported Outcomes, Burdens and Experiences (PROBE) questionnaire can be used to measure quality of life in persons with haemophilia (PWH) and is integrated in the Canadian Bleeding Disorders Registry (CBDR). This offers the opportunity to compare the same data inputted by patients in PROBE and their treating team in CBDR. Our objectives were to assess the feasibility of collecting PROBE data through CBDR and to compare the data collected from these two sources. We conducted a prospective observational study among PWH using MyCBDR. Participants were invited to digitally complete the PROBE questionnaire at baseline and to repeat it at 6 and 12 months. Additional data were passively collected through CBDR. Data from PROBE and CBDR were compared using Kappa agreement, intraclass correlation (ICC) and Pearson correlation. A total of 142 PWH participated. Recruitment ratios were 21.1% and 12.0% for the two phases. Retention rates were 40.8% at 6 months and 32.4% at 12 months. Three hundred thirteen subjects were involved in the comparison between PROBE and CBDR data. The agreement was good to very good (κ > 0.75) or the correlation very strong, with the exception of the history of inhibitor (κ = 0.57), recent bleeds (κ = 0.48) and current treatment regimen (κ = 0.57). The integration of PROBE with CBDR is feasible and PROBE is a reliable tool for routine PRO data collection. Its use in clinical practice may improve data quality and personalized and patient-centred care.

This book is in commemoration of the twentieth anniversary of the Journal of Southeast Asian American Education and Advancement (JSAAEA) and the fiftieth anniversary of Southeast Asian American refugee resettlement in the United States. Pivotal research articles, reviews, and creative works from past issues of JSAAEA have been selected for this volume to document the history and experiences of Cambodian, Laotian, Hmong, and Vietnamese Americans since initial refugee resettlement began in the United States in 1975, as well as the experiences of more recent Southeast Asian immigrant and refugee groups. Reviews of academic books, novels, memoirs, children’s books, and motion pictures further highlight Southeast Asian American perspectives and experiences. Creative works, including poetry and short stories by Cambodian, Laotian, Hmong, Vietnamese, Thai, and Burmese American writers, provide additional and often intimate insights and reflections on the Southeast Asian American experience.

Abstract Recently, there has been a significant push to prepare all engineers with skills in sustainability, motivated by industry needs, accreditation requirements, and international efforts such as the National Science Foundation's 10 Big Ideas and Grand Challenges and the United Nations' Sustainable Development Goals (SDGs). This paper discusses a new toolkit to enable broad dissemination of vetted tools to help engineering faculty members teach sustainability using resources from the Engineering for One Planet (EOP) initiative. This toolkit is to be used as a mechanism to engage a diversity of stakeholders to use their voices, experiences, and connections to share the need for national curricular change in engineering education widely. This toolkit can foster the integration of sustainability-focused learning outcomes into engineering courses and programs. This is particularly important for graduating engineers at this crucial time when we collectively face a convergence of national- and global-scale planetary crises that professional engineers will directly and indirectly impact. Catalyzed by The Lemelson Foundation and VentureWell, the EOP initiative provides teaching tools, grants, and support for the EOP Network —a volunteer action network— comprising diverse stakeholders collectively seeking to transform engineering education to equip all engineers with the understanding, knowledge, skills, and mindsets to ensure their work contributes to a healthy world. The EOP Framework, a fundamental resource of the initiative, provides a curated and vetted list of ABET-aligned sustainability-focused student learning outcomes, including core and advanced. It covers social and environmental sustainability topics and essential professional skills such as communication, teamwork, and critical thinking. It was designed as a practical implementation tool — rather than a research framework — to help educators embed sustainability concepts and tools into engineering courses and programs at all levels. The Lemelson Foundation has provided a range of grants to support curricular transformation efforts using the EOP Framework. With support from The Lemelson Foundation, ASEE launched an EOP Mini-Grant Program in 2022 to engender curricular changes using the EOP Framework. The EOP Network is working to extend the reach of the Framework across the ASEE community beyond initial pilot programs by implementing an EOP Toolkit for EOP Network members and other stakeholders to use at their home institutions, conferences, and informative workshops. This article describes the rationale for creating the EOP Toolkit, the development process, content examples, and use scenarios.

We propose a modified Wasserstein generative adversarial network (M-WGAN) to study the distribution of the topological charge in lattice QCD based on Monte Carlo simulations. We construct new generator and discriminator in M-WGAN to support the generation of high-quality distribution. Our results show that the M-WGAN scheme of machine learning should be helpful for us to calculate efficiently the 1D distribution of topological charge compared with the method by the MC simulation alone.