The technological revolution of the Internet has digitized the social, economic, political, and cultural activities of billions of humans. While researchers have been paying due attention to concerns of misinformation and bias, these obscure a much less researched and equally insidious problem - that of uncritically consuming incomplete information. The problem of incomplete information consumption stems from the very nature of explicitly ranked information on digital platforms, where our limited mental capacities leave us with little choice but to consume the tip of a pre-ranked information iceberg. This study makes two chief contributions. First, we leverage the context of internet search to propose an innovative metric that quantifies information completeness. For a given search query, this refers to the extent of the information spectrum that is observed during web browsing. We then validate this metric using 6.5 trillion search results extracted from daily search trends across 48 nations for one year. Second, we find causal evidence that awareness of information completeness while browsing the Internet reduces resistance to factual information, hence paving the way towards an open-minded and tolerant mindset.

This dataset contains detailed records of public installations of open-source software entities maintained in collaboration with the Public Knowledge Project - Open Journal Systems (OJS), Open Monograph Press (OMP), and Open Preprint Systems (OJS). OJS, for instance, currently enables peer-reviewed research by more than 52000 multilingual journals, largely hosted in the Global South. We release an updated dataset annualy. This dataset is now being ingested by scientometric databases such as Google Scholar and OpenAlex to improve the discoverability of research published beyond traditional Eurocentric commercial publishing channels.

Educational technology is ubiquitous, and educational technology interventions often target literacy. However, there is insufficient information about the effects of these interventions on literacy outcomes in elementary school. Therefore, we conducted a systematic review and meta-analysis of quasi-experimental and experimental studies published between 2010 and 2023 that evaluated the effects of educational technology interventions on the literacy outcomes of K–5 students. The meta-analysis across 119 studies from 105 manuscripts revealed positive effects for decoding (0.33), language comprehension (0.30), reading comprehension (0.23), and writing proficiency (0.81). However, when isolating effects for standardized measures, effects for decoding (0.23), language comprehension (0.12), reading comprehension (0.14), and writing proficiency (0.34) were reduced. Moderator analyses suggested that effects can depend on outcome, intervention, and participant characteristics. In the discussion, we describe and contextualize findings and call for more research to further unpack moderator effects. We also discuss possible implications for practice and policy.

Low-income individuals in developing countries are often inadequately prepared for employment because they lack key labor market skills. We explore how employability and wage outcomes are related to English language skills in a novel, large-scale randomized field experiment conducted in Delhi, India, involving 1,260 low-income individuals. Experimental estimates indicate that a job training program that emphasizes English language skills training substantially increases English language skills as well as employability and estimated wages (as assessed by hiring managers through interviews) for regular jobs and employability for jobs that specifically require English language skills. Program effects hold regardless of gender, social class, or prior employment. We furthermore find that participants enjoy improved employability and estimated wage outcomes because the program improves their English language skills. Taken together, our results suggest that English language skills training, which is surprisingly underutilized in developing countries, may provide considerable economic opportunities for individuals from low-income backgrounds.

The Internet has not only digitized but also democratized information access across the globe. This gradual but path-breaking move to online information propagation has resulted in search engines playing an increasingly prominent role in shaping access to human knowledge. When an Internet user enters a query, the search engine sorts through the hundreds of billions of possible webpages to determine what to show. Google dominates the search engine market, with Google Search surpassing 80% market share globally every year of the last decade. Only in Russia and China do Google competitors claim more market share, with approximately 60% of Internet users in Russia preferring Yandex (compared to 40% in favor of Google) and more than 80% of China’s Internet users accessing Baidu as of 2022. Notwithstanding this long-standing regional variation in Internet search providers, there is limited research showing how these providers compare in terms of propagating state-sponsored information. Our study fills this research gap by focusing on Russian cyberspace and examining how Google and Yandex’s search algorithms rank content from Russian state-controlled media (hereon, RSM) outlets. This question is timely and of practical interest given widespread reports indicating that RSM outlets have actively engaged in promoting Kremlin propaganda in the lead-up to, and in the aftermath of, the Russian invasion of Ukraine in February 2022.

Network psychometrics leverages pairwise Markov random fields to depict conditional dependencies among a set of psychological variables as undirected edge-weighted graphs. Researchers often intend to compare such psychometric networks across subpopulations, and recent methodological advances provide invariance tests of differences in subpopulation networks. What remains missing, though, is an analogue to an effect size measure that quantifies differences in psychometric networks. We address this gap by complementing recent advances for investigating whether psychometric networks differ with an intuitive similarity measure quantifying the extent to which networks differ. To this end, we build on graph-theoretic approaches and propose a similarity measure based on the Frobenius norm of differences in psychometric networks’ weighted adjacency matrices. To assess this measure’s utility for quantifying differences between psychometric networks, we study how it captures differences in subpopulation network models implied by both latent variable models and Gaussian graphical models. We show that a wide array of network differences translates intuitively into the proposed measure, while the same does not hold true for customary correlation-based comparisons. In a simulation study on finite-sample behavior, we show that the proposed measure yields trustworthy results when population networks differ and sample sizes are sufficiently large, but fails to identify exact similarity when population networks are the same. From these results, we derive a strong recommendation to only use the measure as a complement to a significant test for network similarity. We illustrate potential insights from quantifying psychometric network similarities through cross-country comparisons of human values networks.

We present a method to conduct automated surveys over WhatsApp, a globally popular messaging service. WhatsApp surveys offer potential advantages since they incur relatively low costs to respondents and researchers, are easy to use for respondents who are already familiar with WhatsApp chat features, and facilitate continued engagement with mobile populations as users can retain their WhatsApp number even if they change SIM cards and phone numbers. Yet, there is limited knowledge on how well WhatsApp surveys perform empirically. We test the WhatsApp method using two original panel surveys of refugees in Colombia and the U.S. and find satisfactory response rates and retention over a ninemonth follow-up period in these mobile populations. We also conduct a mode experiment in Colombia comparing WhatsApp to short message service (SMS) and interactive voice response (IVR) surveys. We find that WhatsApp had a 12 and 27 percentage points higher response rate than IVR and SMS, respectively, resulting from higher initial engagement and lower break-off. We conclude by discussing advantages and limitations of the WhatsApp method and offer documentation and a public code repository to support researchers and practitioners in applying the method in other contexts.

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 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 Cabell’s Predatory Reports, and 1.4% show up in Beall’s (2021) questionable list. This paper seeks to both contribute to and historically situate the 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.

This chapter presents a three-phase analysis of the 521 journals that use the open source publishing platform Open Journal Systems (OJS) while appearing on Beall’s list of predatory publishers and journals and/or in Cabells Predatory Reports, both of which purport to identify journals that charge authors article processing fees (APC) to publish in the pretense of a peer-reviewed journal. As members of the Public Knowledge Project, which develops this freely available publishing platform, the authors feel a responsibility to explore what platform developers can do to address both the real problem of duplicitous journals and the over-ascription of the “predatory” label to publishers and journals. Drawing on data from the 25,671 journals using OJS in 2020, the chapter represents an assessment and intervention. The first phase involved the researchers reaching out to publishers and editors on Beall’s list using OJS; the second phase involves determining the extent to which journals using OJS appeared on the two predatory lists, and the third reports on a new system, involving trade organizations, such ORCID and Crossref, for authenticating journal practices. The goal is to provide a publicly accessible industry standard for readers to more reliably assess journal quality than depending on two impressionistic lists of predatory journals.

Whether faculty research affects college student learning has long been the subject of debate. Previous studies use subjective measures of student learning; focus on correlation rather than causation; and typically focus on one college, thus lacking generalizability. Using unique, large-scale survey and assessment data that we collected from nationally representative samples of STEM undergraduates in China, India, and Russia, as well as a causal identification strategy that accounts for differential sorting of students to faculty, we present generalizable estimates of the effect of faculty research on objective, standardized measures of student learning. Results show that faculty research has a negative effect on student learning, suggesting direct trade-offs between the university’s dual mission of producing research and learning.