What do the differences and commonalities in doctoral dissertation acknowledgments across disciplines reveal?

Area of Science:

• Scholarly Communication
• Computational Linguistics
• Academic Writing Analysis

Background:

• Acknowledgments in academic dissertations are under-researched, particularly regarding disciplinary variations.
• Previous studies focused on funding, genre, and linguistic features of acknowledgments.
• Understanding acknowledgment patterns offers insights into academic culture and researcher expression.

Purpose of the Study:

• To analyze the content and stylistic features of acknowledgments in Chinese doctoral dissertations across broad disciplinary categories.
• To identify disciplinary-specific topics and writing styles within acknowledgments.
• To explore the influence of academic training on the formation of acknowledgment patterns.

Main Methods:

• BERTopic modeling to identify key topics within acknowledgments.
• Computational linguistics techniques for quantitative evaluation of content and style.
• Hierarchical clustering analysis to assess cross-disciplinary similarities.

Main Results:

• Topic modeling revealed frequent emotional reflections and distinct disciplinary characteristics in Chinese doctoral acknowledgments.
• Hierarchical clustering demonstrated that similar disciplines share greater similarity in acknowledgment content and writing style.
• Findings suggest academic training significantly shapes researchers' writing practices in acknowledgments.

Conclusions:

• Dissertation acknowledgments serve as a valuable site for studying disciplinary acculturation and researcher identity.
• The study provides empirical evidence for the influence of academic fields on scholarly writing conventions.
• Further interdisciplinary research, including psychological and cross-cultural perspectives, is recommended.