This project, funded by the NL eScience Center, applies state-of-the-art machine learning techniques to study conceptual change over time. Building on the BERT architecture, which has recently revolutionized computational language understanding, we aim to create a series of chronologically ordered models based on historical Dutch textual data. Using vast archives from the Dutch National Library—including newspapers, magazines, and books—this project traces the evolution of Dutch words and their meanings in public discourse from the Second World War to the present day.Our primary goal is to study the conceptual history of one of today’s most pressing issues: global sustainability. By analyzing historical shifts in discourse, we aim to uncover the continuities and disruptions that shape the conversation around sustainability. Through these insights, we seek to better understand the forces influencing how sustainability has been discussed over time.
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