Hitesh Goel  supervised by Dr. Aniket Alam  received his Master of Science –  Dual Degree in Computing & Human Sciences (CHD). Here’s a summary of his research work on From Archives to Algorithms: Tracing Trade and Pastoral Transitions in the Western Himalayas:

This thesis is situated in the intersection of History and Natural Language Processing, and examines the historical transformation of trade and pastoralism in the Western Himalayas, while also introducing PastPaths, a domain-adapted Natural Language Processing (NLP) pipeline developed to extract and visualize historical trade networks from unstructured textual archives. The Western Himalayan region, including present-day Kashmir, Ladakh, Himachal Pradesh, and parts of Tibet, has historically functioned as a dense zone of exchange, sustained by the movements of pastoralist communities and transregional trade in commodities such as wool, salt, and grain. Existing historiography has primarily framed the twentieth-century decline of pastoralism as a top-down consequence of colonial policies, often sidelining the agency of local communities. Through a close reading of archival records, census data, and ethnographic literature, this thesis offers a more nuanced account by highlighting how pastoralists strategically adapted to shifting political and economic contexts. These transitions included movement into agriculture, wage labor, and state employment. From a computational perspective, the thesis addresses limitations in historical methodology by developing PastPaths, a pipeline that integrates OCR, Named Entity Recognition (NER), relation extraction, and map generation. The pipeline uses transformer-based models fine-tuned on manually annotated data from the region and is capable of extracting structured information about the trade of commodities. Our results show improved performance over baseline models and demonstrate the feasibility of large-scale trade reconstruction in data-scarce settings. The thesis contributes to both historical and computational research. It reframes dominant narratives about pastoral decline by recovering voices of adaptation and choice, while also showcasing how domain-specific NLP tools can meaningfully support historical inquiry. More broadly, it advances the field of computational humanities by proposing an integrative workflow where historical questions guide model design, and computational outputs inform previous scholarship in the human sciences. This approach offers a framework for bridging disciplinary silos and generating new insights from underexplored textual archives.

July 2025