The debate over the science of reading has focused primarily on decoding (i.e., connecting letters and sounds to read words) and whether to use phonics to teach it. However, research on reading has included much more than decoding. Language comprehension, which allows readers to derive meaning from text, is an equally critical component of reading. Research has suggested that explicit instruction on the components of language comprehension—vocabulary and semantics, morphology, and syntax—can support language and reading comprehension. To inform the field on the science of reading as it pertains to language comprehension, in this meta‐analysis of recent language comprehension interventions (n = 43) in U.S. elementary schools, the authors examined whether effects vary depending on participant and intervention characteristics. Findings suggest positive effects on custom measures of vocabulary, listening comprehension, and reading comprehension but not on standardized measures of these outcomes. Results also indicate positive effects for English learners and promise for multicomponent interventions and those that include technology. Much more research is needed on how best to support language comprehension for underserved populations (e.g., students from low‐income backgrounds) and how interventions can be optimized to support generalizable language and literacy outcomes. Implications for policy and practice are discussed.

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We assess and compare computer science skills among final-year computer science undergraduates (seniors) in four major economic and political powers that produce approximately half of the science, technology, engineering, and mathematics graduates in the world. We find that seniors in the United States substantially outperform seniors in China, India, and Russia by 0.76–0.88 SDs and score comparably with seniors in elite institutions in these countries. Seniors in elite institutions in the United States further outperform seniors in elite institutions in China, India, and Russia by ∼0.85 SDs. The skills advantage of the United States is not because it has a large proportion of high-scoring international students. Finally, males score consistently but only moderately higher (0.16–0.41 SDs) than females within all four countries.

By analyzing 25,671 journals largely absent from common journal counts, as well as Web of Science and Scopus, this study demonstrates that scholarly communication is more of a global endeavor than is commonly credited. These journals, employing the open source publishing platform Open Journal Systems (OJS), have published 5.8 million items; they are in 136 countries, with 79.9% in the Global South and 84.2% following the OA diamond model (charging neither reader nor author). A substantial proportion of journals operate in more than one language (48.3%), with research published in a total of 60 languages (led by English, Indonesian, Spanish, and Portuguese). The journals are distributed across the social sciences (45.9%), STEM (40.3%), and the humanities (13.8%). For all their geographic, linguistic, and disciplinary diversity, 1.2% are indexed in the Web of Science and 5.7% in Scopus. On the other hand, 1.0% are found in Cabells Predatory Reports, while 1.4% show up in Beall’s questionable list. This paper seeks to both contribute and historically situate expanded scale and diversity of scholarly publishing in the hope that this recognition may assist humankind in taking full advantage of what is increasingly a global research enterprise. Peer Review https://publons.com/publon/10.1162/qss_a_00228

We present a method to conduct automated surveys over WhatsApp, a popular cross-platform messaging service. The method relies on a combination of the WhatsApp Business, Twilio, and Google APIs to design the survey flow, send and receive survey messages automatically, and facilitate data processing. Respondents complete the survey entirely within the WhatsApp application in the form of a chat conversation. WhatsApp surveys incur relatively low costs to both respondents and researchers and facilitate continued engagement with mobile populations as users can retain their WhatsApp number even if they change SIM cards and phone numbers. We describe the use of this method with two case studies where we surveyed refugees and migrants in Colombia, as well as resettled refugees in the U.S. The case studies offer preliminary evidence that automated surveys over WhatsApp provide a viable alternative for surveying and panel data collection. While the method is not without limitations, it offers a promising research tool with opportunities for diverse implementation and empirical study given the widespread global use of WhatsApp. We o?er documentation and a public code repository as supplementary materials to support researchers in applying this method in other contexts.