Effects of teaching experience and culture on choral directors' descriptions of choral tone.

In this study we examine the effects of experience and culture on choral teachers' description of choral tone across a range of genres. What does a "good" choral music performance sound like? Is there an objective standard of performance excellence, or is beauty in the eye of the beholder? In teacher preparation programs, choral directors in the United States have been taught to identify and teach particular, culturally-bounded standards of choral tone in their students. Choral directors evaluate their students' voices along two dimensions: health and appropriateness. They discern and describe whether the student's musical instrument-their voice-is producing sound in a healthy and non-damaging way. They also judge whether the style of their sound is appropriate for the music they are singing. However, teacher preparation programs do not provide common standards or lexicon for describing tone. This may increase implicit bias of individual directors, and inadvertently exacerbate ethnocentrism and harm students' self-perception. Using a computational text analysis approach, we evaluate the content of open-ended survey responses from teachers, finding that the language used to describe and rate choral performance varies by experience, and by the choral selection (e.g., whether it is a traditional Western or non-Western song). We suggest that regularizing the terminology and providing common training through professional organizations can minimize potential bias and generate more systematic, precise use of qualitative descriptors of health and appropriateness, which will benefit students and teachers.

Online social cohesion reflects real-world group action in Syria during the Arab Spring.

In recent years, political activists have taken to social media platforms to rapidly reach broad audiences. Despite the prevalence of micro-blogging in these sociopolitical movements, the degree to which virtual mobilization reflects or drives real-world movements is unclear. Here, we explore the dynamics of real-world events and Twitter social cohesion in Syria during the Arab Spring. Using the nonlinear methods cross-recurrence quantification analysis and windowed cross-recurrence quantification analysis, we investigate if frequency of events of different intensities are coupled with social cohesion found in Syrian tweets. Results indicate that online social cohesion is coupled with the counts of all, positive, and negative events each day but shows a decreased connection to negative events when outwardly directed events (i.e., source events) were considered. We conclude with a discussion of implications and applications of nonlinear methods in political science research.

Women and Minorities Encouraged to Apply (Not Stay).

The Coronavirus 2019 (COVID-19) pandemic has deepened gender and racial diversity problems in academia. Mentorship shows women and other under-represented groups where the ladders to success are, and helps them avoid the chutes, a revised leaky pipeline metaphor. Here, we identify tangible strategies that will improve gender equity, including increasing active mentorship by male academics.

Validating the eHealth Literacy Scale in Rural Adolescents.

PURPOSE: Given that the recent eHealth literacy literature supports the properties of the 3-factor eHealth literacy scale (eHEALS) model in samples with millennials, adults, and older adults, the appropriate next step is to establish whether the model can be reproduced in a rural adolescent sample. The purpose of this study was to evaluate the recent 3-factor model by Paige and associates with a sample of seventh-grade students. METHODS: This cross-sectional study included a subsample of students (n = 146) from 3 school districts in Appalachian Kentucky. We used confirmatory factor analysis (CFA) procedures and small sample model fit guidelines to evaluate our model, and the 1-sample bootstrap algorithm with bias-corrected and accelerated 95% confidence intervals to estimate associations among eHEALS and health and technology variables. FINDINGS: A total of 137 students, or 61% of enrolled seventh-grade students, completed the study. CFA results showed eHEALS 3-factor loadings-information awareness, information seeking, and information engagement-were high (≥0.63) and statistically significant. We observed evidence of a good model fit (root mean square error of approximation [RMSEA] = 0.07, standardized root mean square residual [SRMR] = 0.03, comparative fit index [CFI] = 0.99) and results are comparable with Paige and associates' model fit (RMSEA = 0.06, SRMR = 0.08, CFI = 0.98). Correlations showed that students with more access to technology were associated with higher information seeking (r = 0.31) and higher information engagement (r = 0.23). eHealth literacy scores did not differ by level of rurality or gender. CONCLUSIONS: The 3-factor eHEALS is a reliable and valid instrument in assessing eHealth literacy in a group of rural seventh graders from Appalachian Kentucky.

Gender in the time of COVID-19: Evaluating national leadership and COVID-19 fatalities.

In this paper we explore whether countries led by women have fared better during the COVID-19 pandemic than those led by men. Media and public health officials have lauded the perceived gender-related influence on policies and strategies for reducing the deleterious effects of the pandemic. We examine this proposition by analyzing COVID-19-related deaths globally across countries led by men and women. While we find some limited support for lower reported fatality rates in countries led by women, they are not statistically significant. Country cultural values offer more substantive explanation for COVID-19 outcomes. We offer several potential explanations for the pervasive perception that countries led by women have fared better during the pandemic, including data selection bias and Western media bias that amplified the successes of women leaders in OECD countries.

Automated content analysis across six languages.

Corpus selection bias in international relations research presents an epistemological problem: How do we know what we know? Most social science research in the field of text analytics relies on English language corpora, biasing our ability to understand international phenomena. To address the issue of corpus selection bias, we introduce results that suggest that machine translation may be used to address non-English sources. We use human translation and machine translation (Google Translate) on a collection of aligned sentences from United Nations documents extracted from the Multi-UN corpus, analyzed with a "bag of words" analysis tool, Linguistic Inquiry Word Count (LIWC). Overall, the LIWC indices proved relatively stable across machine and human translated sentences. We find that while there are statistically significant differences between the original and translated documents, the effect sizes are relatively small, especially when looking at psychological processes.