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.

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.