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Anticipating Job Market Demands—A Deep Learning Approach to Determining the Future Readiness of Professional Skills

Authors :
Albert Weichselbraun
Norman Süsstrunk
Roger Waldvogel
André Glatzl
Adrian M. P. Braşoveanu
Arno Scharl
Source :
Future Internet, Vol 16, Iss 5, p 144 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

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.

Details

Language :
English
ISSN :
19995903
Volume :
16
Issue :
5
Database :
Directory of Open Access Journals
Journal :
Future Internet
Publication Type :
Academic Journal
Accession number :
edsdoj.fb21e20dd24e4c079787fce079bbfd72
Document Type :
article
Full Text :
https://doi.org/10.3390/fi16050144