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The ongoing debate about the value of higher education has expanded beyond its prevailing focus on economic development and professional training to emphasize its role in shaping well-rounded citizens. As a global model of higher education and a hallmark of the American system, liberal arts education has long been recognized for fostering students’ holistic development and active civic engagement. However, empirical evidence from longitudinal data remains limited, particularly regarding the specific elements of liberal arts education that most effectively cultivate engaged citizens. This study examines the impact of liberal arts education on graduates’ civic and democratic beliefs and behaviors. Using longitudinal data from 2,585 alumni across seven public four-year colleges and universities in the United States, this quantitative analysis leverages a secondary dataset that includes students’ campus experiences and survey responses collected ten years after graduation to identify key factors contributing to generative behavior and political engagement. The findings reveal that specific components of liberal arts education—such as diversity-focused courses, participation in service-learning programs, and involvement in student organizations—play a significant role in fostering active and engaged citizenship. The study provides empirical evidence of higher education’s role in preparing graduates for civic and democratic life, equipping them to address social challenges. These findings underscore the broader implications of higher education for personal development for civic outcomes and democratic citizenship, particularly at a time when policymakers and governments are increasingly questioning its value and reducing funding.
Measurements of semi-inclusive deep-inelastic scattering multiplicities for pi(+) and pi(-) from proton and deuteron targets are reported on a grid of hadron kinematic variables z, P-T, and & varphi;* for leptonic kinematic variables in the range 0.3 < x < 0.6 and 3 < Q(2) < 5GeV(2). Data were acquired in 2018 and 2019 at Jefferson Lab Hall C with a 10.6 GeV electron beam impinging on 10-cm-long liquid hydrogen and deuterium targets. Scattered electrons and charged pions were detected in the High Momentum Spectrometer and Super High Momentum Spectrometer, respectively. The multiplicities were fitted for each bin in (x, Q(2), z, Pt) to extract the & varphi;*-independent M-0 and the azimuthal modulations < cos(& varphi;*)> and < cos(2 & varphi;*)>. The Pt dependence of the M-0 results was found to be remarkably consistent for the four cases studied: ep -> e pi(+) X, ep -> e pi(-) X, ed -> e pi X+, ed -> e pi X- over the range 0 GeV < P-t < 0.4GeV, as were the multiplicities evaluated near & varphi;*=180(degrees) over the extended range 0GeV < P-t < 0.7GeV. The Gaussian widths of the P-t dependence exhibit a quadratic increase with z. The cos(& varphi;*) modulations were found to be consistent with zero for pi(+), in agreement with previous world data, while the pi(zs) moments were, in many cases, significantly greater than zero. The cos(2 & varphi;*) modulations were found to be consistent with zero. The higher statistical precision of this dataset of about 20 000 individual multiplicity values, compared with previously published data, should allow improved determinations of quark transverse momentum distributions and higher twist contributions.
Ensuring safe and effective use of artificial intelligence (AI) requires understanding and anticipating its performance on new tasks, from advanced scientific challenges to transformed workplace activities1-3. So far, benchmarking has guided progress in AI but has offered limited explanatory and predictive power for general-purpose AI systems4-8, attributed to limited transferability across specific tasks9-11. Here we introduce general scales for AI evaluation that elicit demand profiles explaining what capabilities common AI benchmarks truly measure, extract ability profiles quantifying the general strengths and limits of AI systems and robustly predict AI performance for new task instances. Our fully automated methodology builds on 18 rubrics, capturing a broad range of cognitive and intellectual demands, which place different task instances on the same general scales, illustrated on 15 large language models (LLMs) and 63 tasks. Both the demand and the ability profiles on these scales bring new insights such as construct validity through benchmark sensitivity and specificity and explain conflicting claims about whether AI has reasoning capabilities. Ultimately, high predictive power at the instance level becomes possible using the general scales, providing superior estimates over strong black-box baseline predictors, especially in out-of-distribution settings (new tasks and benchmarks). The scales, rubrics, battery, techniques and results presented here constitute a solid foundation for a science of AI evaluation, underpinning the reliable deployment of AI in the years ahead.
This study comprehensively examines the effect of the College Scorecard (the Scorecard), first launched in 2015, on student application and enrollment at 4-year universities in the United States by reported earnings level and sector. Using institution-year panel data from the Integrated Postsecondary Education Data System, we employed a regression discontinuity in time design and an event study analysis to examine whether application and enrollment trends at 4-year institutions shifted after the release of the Scorecard. Results indicate little evidence of shifts: students did not substantially move away from lower-earning institutions or sort into higher-earning ones, and the null findings remain across earning levels and sectors. These findings align with early studies showing limited influence of the Scorecard on college choice. Finally, we conclude our manuscript by pointing out that the National Center for Education Statistics’ federal, individual-level longitudinal surveys, which are currently suspended, will serve as critical data sources for future evaluation efforts on the Scorecard and other related federal policies, including the new accountability framework recently introduced by the One Big Beautiful Bill Act. We recommend lifting the suspension of these individual-level longitudinal surveys.
Prior scholarly literature has investigated definitions of mindfulness, yet limited research has examined how contemporary mindfulness is culturally constructed among lay individuals (i.e., non-academic researchers). The present mixed-methods study sought to provide an initial mapping of the context of ideas and cultural orientations that inform lay people’s understanding of mindfulness. Further, we assessed how one’s level of contemplative experience informs qualitative definitions of mindfulness among US participants. Participants were also asked quantitative questions about their views of mindfulness and their understanding of the relationship between “mindfulness” and “meditation”. Our analytic sample (n = 100; 61.6
