  Language models increasingly rely on massive web dumps for diverse text data.
However, these sources are rife with undesirable content. As such, resources
like Wikipedia, books, and newswire often serve as anchors for automatically
selecting web text most suitable for language modeling, a process typically
referred to as quality filtering. Using a new dataset of U.S. high school
newspaper articles -- written by students from across the country -- we
investigate whose language is preferred by the quality filter used for GPT-3.
We find that newspapers from larger schools, located in wealthier, educated,
and urban ZIP codes are more likely to be classified as high quality. We then
demonstrate that the filter's measurement of quality is unaligned with other
sensible metrics, such as factuality or literary acclaim. We argue that
privileging any corpus as high quality entails a language ideology, and more
care is needed to construct training corpora for language models, with better
transparency and justification for the inclusion or exclusion of various texts.
