A Multidimensional Analysis of the Register of Academic English in Forestry: A Corpus-Based Study and Its Implications for Teaching

This study employs a corpus-based multidimensional analysis approach to systematically investigate the register of academic English in forestry, aiming to reveal its linguistic features and explore the implications for EAP teaching. Based on a self-compiled corpus of SCI papers in forestry, linguistic features were extracted using the MAT tool, and the dimension scores of the five primary dimensions were analyzed and compared with those of a General English corpus. The results indicate that forestry academic English exhibits features of high informational density, non-narrativity, explicit reference, low persuasive intent, and abstractness, overall aligning closely with an “Learned Exposition” style. Z-score analysis highlights overused features (e.g., PHC, SERE, NN) and underused features (e.g., RB, TPP3), emphasizing objectivity and informational density, which contrasts with the balanced nature of General English. These features reflect the specialized nature of the forestry register. Pedagogical implications include adjusting learning objectives to focus on professional expression, integrating corpus-driven content, utilizing tools like MAT and AI to analyze student writing, and quantitatively assessing dimension scores to facilitate learners’ transition and enhance their capabilities in international academic communication. 本研究采用基于语料库的多维分析方法，对林学学术英语语域进行系统考察，旨在揭示其语言特征并探讨对EAP教学的启示。基于自建林学SCI论文语料库，运用MAT工具提取语言特征，分析5个主要维度得分，并与通用英语语料进行对比。结果显示：林学学术英语呈现高度信息密集、非叙述性、明晰指称、低劝说意图和抽象化特征，整体接近“学术性阐述”风格。Z-score分析突出过度使用特征（如PHC、SERE、NN）和不足使用特征（如RB、TPP3），强调客观性和信息密度，与通用英语的平衡性形成对比，这些特征反映了林学语域的专业化。教学启示包括调整目标聚焦专业表达、融入语料库驱动内容、采用MAT和AI工具分析学生写作，并量化评估维度得分，以助力学习者转型，提升国际学术交流能力。