Document Type : Original Article
Authors
1
Ministry of Science, Research and Technology, Tehran, Iran
2
Advanced Technologies in Medicines Dept., Iran University of Medical Science
Abstract
The digital revolution has ushered in a transformative era in humanities research, marked by cutting-edge technologies and a paradigm shift in how studies are conceptualized. This evolution is evident in the altered nature, volume, size, and dimensions of data, presenting both unprecedented challenges and opportunities. The rise of artificial intelligence methodologies has created a dynamic landscape, compelling researchers to navigate a crossroads in determining the interplay between machine learning and traditional statistical approaches. A pivotal factor influencing this shift is the sheer abundance of contemporary data. The digital age has propelled us into an era where data is generated on an unparalleled scale, posing challenges related to storage, processing, and analysis. Traditional tools are inadequate in the face of this data deluge, prompting researchers to embrace advanced techniques capable of grappling with the intricacies of vast and diverse datasets. Simultaneously, the nature of the data has undergone a metamorphic evolution. Modern data, no longer confined to structured formats, is often unstructured, heterogeneous, and complex. This shift necessitates a departure from traditional analytical approaches, fostering a growing reliance on methodologies adept at extracting insights from unstructured and diverse data. Machine learning, with its capacity to discern patterns in complex datasets without explicit programming, emerges as a potent ally in this endeavor. However, the ascendancy of machine learning in humanities research is not without challenges, with interpretability being a chief concern. The intricate and often opaque nature of machine learning models poses challenges in deciphering the rationale behind their predictions, raising questions about the reliability and accountability of findings, especially in disciplines where transparency is paramount. On the opposite spectrum lies the steadfast realm of traditional statistical approaches, grounded in established principles of statistical inference, offering a transparent and hypothesis-driven framework. While these methods have been the foundation of scientific inquiry, excelling in structured data scenarios, they encounter limitations when faced with the complexities of unstructured and extensive datasets. In light of these considerations, researchers find themselves at a crossroads, evaluating the strengths and limitations of machine learning against the reliability and transparency of traditional statistical methods. This article provides a comprehensive exploration of this dichotomy, delving into multifaceted perspectives shaping the ongoing discourse on the application of these paradigms in humanities research. From the intricacies of data characteristics to the ethical implications of analytical choices, the comparative analysis aims to furnish researchers with valuable insights for navigating the intricate landscape of contemporary humanities data analysis.
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