Abstract
In this study, we propose a structured methodology that utilizes large language models (LLMs) in a cost-efficient and parsimonious manner, integrating the strengths of scholars and machines while offsetting their respective weaknesses. Our methodology, facilitated through a chain of thought and few-shot learning prompting from computer science, extends best practices for co-author teams in qualitative research to human-machine teams in quantitative research. This allows humans to utilize abductive reasoning and natural language to interrogate not just what the machine has done but also what the human has done. Our method highlights how scholars can manage inherent weaknesses OF LLMs using careful, low-cost techniques. We demonstrate how to use the methodology to interrogate human-machine rating discrepancies for a sample of 1,934 press releases announcing pharmaceutical alliances (1990-2017).
Abstract (translated)
在这项研究中，我们提出了一种结构化的方法论，该方法利用大型语言模型（LLMs），以成本效益高且简洁的方式运作，并整合学者和机器的优势，同时弥补它们各自的不足。我们的方法论通过计算机科学中的思维链和少量样本学习提示来实现，将质性研究中最佳的合作作者团队实践扩展到了量化研究中的人机团队合作。这种方法使得人类能够利用溯因推理和自然语言，不仅质疑机器做了什么，还能质疑人自己做了什么。我们提出的方法强调了学者如何通过仔细且低成本的技术手段来管理LLMs固有的弱点。我们展示了如何使用该方法论来审问一组1,934份宣布医药联盟的新闻稿（1990-2017年）中的人机评分差异。
URL
https://arxiv.org/abs/2512.07583
