本文基于两个百万词规模的翻译英语和原创英语可比语料库，采用机器学习方法分析英语翻译与原创文本的96个语言特征，比较不同类型的原语在多个语域中对翻译英语的“原语透过效应”。研究发现：1）来自不同类型原语的翻译英语整体上与原创英语存在显著区别；2）相较于形态距离，语言地位对翻译英语的原语透过效应更为显著；3）在不同语域中，语言地位和形态距离对翻译语言特征的影响呈现竞争关系。这些发现揭示了翻译共性形成的复杂机制，为译者在应对不同原语类型、相对地位和语域差异时进行有效语言调适提供了理论支持。

This study employs two multi-million-word corpora of comparable translated and non-translated/original English texts to examine the “source language shining-through” effect. Utilizing machine learning techniques to analyze 96 linguistic features, it compares translations from various source languages across multiple registers. The results reveal that (1) translated English consistently differs from original English, regardless of source language; (2) language status exerts a greater influence on the shining-through effect than typological distance; and (3) language status and typological distance interact competitively across registers. These findings advance our understanding of translation universals and provide theoretical guidance for translators seeking to adapt effectively to source language typology, language status, and register-specific demands.