  Supervised machine learning, in which models are automatically derived from
labeled training data, is only as good as the quality of that data. This study
builds on prior work that investigated to what extent 'best practices' around
labeling training data were followed in applied ML publications within a single
domain (social media platforms). In this paper, we expand by studying
publications that apply supervised ML in a far broader spectrum of disciplines,
focusing on human-labeled data. We report to what extent a random sample of ML
application papers across disciplines give specific details about whether best
practices were followed, while acknowledging that a greater range of
application fields necessarily produces greater diversity of labeling and
annotation methods. Because much of machine learning research and education
only focuses on what is done once a "ground truth" or "gold standard" of
training data is available, it is especially relevant to discuss issues around
the equally-important aspect of whether such data is reliable in the first
place. This determination becomes increasingly complex when applied to a
variety of specialized fields, as labeling can range from a task requiring
little-to-no background knowledge to one that must be performed by someone with
career expertise.
