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Anticipating the demand for professional job market skills needs to consider trends such as automation, offshoring, and the emerging Gig economy, as they significantly impact the future readiness of skills. This article draws on the scientific literature, expert assessments, and deep learning to estimate two indicators of high relevance for a skill’s future readiness: its automatability and offshorability. Based on gold standard data, we evaluate the performance of Support Vector Machines (SVMs), Transformers, Large Language Models (LLMs), and a deep learning ensemble classifier for propagating expert and literature assessments on these indicators of yet unseen skills. The presented approach uses short bipartite skill labels that contain a skill topic (e.g., “Java”) and a corresponding verb (e.g., “programming”) to describe the skill. Classifiers thus need to base their judgments solely on these two input terms. Comprehensive experiments on skewed and balanced datasets show that, in this low-token setting, classifiers benefit from pre-training and fine-tuning and that increased classifier complexity does not yield further improvements.

Recent developments in the fields of computer science, such as advances in the areas of big data, knowledge extraction, and deep learning, have triggered the application of data-driven research methods to disciplines such as the social sciences and humanities. This article presents a collaborative, interdisciplinary process for adapting data-driven research to research questions within other disciplines, which considers the methodological background required to obtain a significant impact on the target discipline and guides the systematic collection and formalization of domain knowledge, as well as the selection of appropriate data sources and methods for analyzing, visualizing, and interpreting the results. Finally, we present a case study that applies the described process to the domain of communication science by creating approaches that aid domain experts in locating, tracking, analyzing, and, finally, better understanding the dynamics of media criticism. The study clearly demonstrates the potential of the presented method, but also shows that data-driven research approaches require a tighter integration with the methodological framework of the target discipline to really provide a significant impact on the target discipline.