In this study, we employ a classification approach to show that different
categories of literary "quality" display unique linguistic profiles, leveraging
a corpus that encompasses titles from the Norton Anthology, Penguin Classics
series, and the Open Syllabus project, contrasted against contemporary
bestsellers, Nobel prize winners and recipients of prestigious literary awards.
Our analysis reveals that canonical and so called high-brow texts exhibit
distinct textual features when compared to other quality categories such as
bestsellers and popular titles as well as to control groups, likely responding
to distinct (but not mutually exclusive) models of quality. We apply a classic
machine learning approach, namely Random Forest, to distinguish quality novels
from "control groups", achieving up to 77\% F1 scores in differentiating
between the categories. We find that quality category tend to be easier to
distinguish from control groups than from other quality categories, suggesting
than literary quality features might be distinguishable but shared through
quality proxies.

研究使用分类方法，展示不同类别的文学 “质量” 显示出独特的语言特征，在分析了包括诺顿选集、企鹅经典系列、开放教材项目等作品的语料库的基础上，对比了当代畅销书、诺贝尔奖得主以及获奖文学作品。我们的分析表明，经典文学作品和高雅作品相对于其他质量类别如畅销书和流行作品以及对照组展现出不同的文本特征，可能是为了回应不同的质量模式。我们采用经典的机器学习方法，即随机森林，来区分质量小说和 “对照组”，在区分这些类别时的 F1 得分达到 77%。我们发现，质量类别相对于对照组更容易区分，而与其他质量类别相比较难区分，这表明文学质量特征可能是可辨识的，但通过质量代理人共享。