## 1 Introduction  
Automation, offshoring, and the emerging Gig economy instigate and intensify labor market disruptions. The ongoing trends of automating repetitive tasks and offshoring continue to reshape traditional job roles and workforce dynamics, changing skill requirements in the labor market. Research by [PERSON] et al. [1] indicates that 60% of occupations have at least 30% work activities that could be automated.  
Automation has triggered a growing demand for technical skills such as data analysis, artificial intelligence, and machine learning, while offshoring amplifies the relevance of intercultural communication and global collaboration skills. The rise of the Gig economy accentuates the significance of adaptability, self-management, and entrepreneurship, particularly as individuals navigate short-term projects and roles. Gig economy platforms like Fiverr, UpWork, and Freelancer, compel both employers and employees to adapt to more flexible working structures. However, as certain tasks become automated or outsourced, routine skills for repetitive operations witness declining demand. This underscores the need for continuous upskilling and reskilling in increasingly competitive job markets.  
This article aims to provide decision-makers with insights into the future readiness of professional skills. The presented approach resonates with the objectives outlined in the United Nations Sustainable Development Goals (SDGs) ([[https://sdgs.un.org/goals](https://sdgs.un.org/goals)]([https://sdgs.un.org/goals](https://sdgs.un.org/goals)), accessed on 1 March 2024) by (i) providing information on a skill's future readiness to guide educational activities and increasing the number of people with relevant skills (SDG 4: Equitable Quality Education) and (ii) helping to better align skill supply and demand to promote sustainable economic growth and productive employment (SDG 8: Decent Work and Economic Growth; see Section 7).  
Evaluating a skill's future readiness in terms of its resilience to automation and outsourcing requires the development of classifiers capable of automatically assessing the skill across these two dimensions. The presented work draws upon a skill ontology that characterizes skills with bipartite skill labels consisting of skill topic and skill verb (e.g., _Java[topic] Programming[verb]_). These labels, more detailed than topic-only labels, still lack additional context and challenge classifiers due to their reliance on just two topics for predictions. This impacts the design and effectiveness of classifiers, as ensemble models might fail to improve the outcome with such limited input.  
Building on previous model comparisons for assessing the future readiness of skills [2], the presented research has been conducted within the Future of Work project ([[https://semanticlab.net/future-of-work](https://semanticlab.net/future-of-work)]([https://semanticlab.net/future-of-work](https://semanticlab.net/future-of-work)), accessed on 1 March 2024), which investigates the performance of multiple classification approaches (Support Vector Machines (SVMs), Transformers, Large Language Models (LLMs), and a deep learning ensemble classifier) toward reliably propagating expert and literature assessments on automatability and offshorability to yet unseen skills.  
The remainder of this paper is structured as follows: Section 2 provides an overview of related work which is followed by a description of the methods applied in this study (Section 4). Section 5 presents the gold standard datasets, evaluation settings, and evaluation results, followed by the conclusion and outlook in Section 7.