Our library is the start of a new alternative repository of stock images. They are available for anyone to use for free under CC licences, or just as inspiration for more helpful and diverse representations of AI.
Shared by Bryan Alexander, 16 saves total
Fertility levels have greatly decreased in virtually every nation in the world, but the timing of the decline has differed even among developed countries. In Europe, Asia, and North America, total fertility rates of some nations dipped below the magic replacement figure of 2.1 as early as the 1970s. But in other nations, fertility rates remained substantial until the 1990s but plummeted subsequently. This paper addresses why some countries in Europe and Asia with moderate fertility levels in 1980s, have become the “lowest-low” nations today (total fertility rates of less than 1.3), whereas those that decreased earlier have not. Also addressed is why the crossover point for the two groups of nations was around the 1980s and 1990s. An important factor that distinguishes the two groups is their economic growth in the post WWII decades, especially the 1960s and the 1970s. Countries with “lowest low” fertility rates today experienced rapid growth in GNP per capita after a long period of stagnation or decline. They were catapulted into modernity, but the beliefs, values, and traditions of their citizens changed more slowly. Thus, swift economic change may lead to both generational and gendered conflicts that result in a rapid decrease in the total fertility rate.
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Archival and experimental research reveals a subtle but persistent ironic consequence: White liberals self-present less competence to minorities than to other Whites-that is, they patronize minorities stereotyped as lower status and less competent.
Shared by Bryan Alexander, 1 save total
Web browser game letting players pick budget priorities.
Shared by Bryan Alexander, 16 saves total
Very short sims for current events and history.
Shared by Bryan Alexander, 1 save total
We leverage a unique national dataset of 41,359 college applications to prospectively predict 4-year bachelor's graduation in a generalizable manner. Our features include sociodemographics, institutional graduation rates, academic achievement, standardized test scores, engagement in extracurricular activities, work experiences, and ratings by teachers and high-school guidance counselors. A random forest classifier successfully predicted 4-year graduation for 71.4% of the students (base rate = 44%) using all 166 of the aforementioned features and a split-half validation method. A stochastic hill-climbing feature selection procedure effectively maintained the same classification accuracy, but with a minimal set of 37 features, consisting of an approximately equal representation of sociodemographics, cognitive, and noncognitive factors. We advocate against using these results for admissions decisions, instead contemplating how they might be used to provide parents and educators with actionable information to guide students towards college success.
Shared by Bryan Alexander, 1 save total
Here we report a randomized, controlled trial measuring college students’ learning and their perceptions when content is presented through an AI-powered tutor compared with an active learning class. The novel design of the custom AI tutor is informed by the same pedagogical best practices as employed in the in-class lessons. We find that students learn significantly more in less time when using the AI tutor, compared with the in-class active learning. They also feel more engaged and more motivated. These findings offer empirical evidence for the efficacy of a widely accessible AI-powered pedagogy in significantly enhancing learning outcomes, presenting a compelling case for its broad adoption in learning environments.
Shared by Bryan Alexander, 4 saves total
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