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

Albert Weichselbraun (), Norman Süsstrunk, Roger Waldvogel, André Glatzl, Adrian M. P. Braşoveanu and Arno Scharl
Additional contact information
Albert Weichselbraun: Swiss Institute for Information Research, University of Applied Sciences of the Grisons, Pulvermühlestrasse 57, 7000 Chur, Switzerland
Norman Süsstrunk: Swiss Institute for Information Research, University of Applied Sciences of the Grisons, Pulvermühlestrasse 57, 7000 Chur, Switzerland
Roger Waldvogel: Swiss Institute for Information Research, University of Applied Sciences of the Grisons, Pulvermühlestrasse 57, 7000 Chur, Switzerland
André Glatzl: Swiss Institute for Information Research, University of Applied Sciences of the Grisons, Pulvermühlestrasse 57, 7000 Chur, Switzerland
Adrian M. P. Braşoveanu: Research Center of New Media Technology, Modul University Vienna, Am Kahlenberg 1, 1190 Vienna, Austria
Arno Scharl: webLyzard technology, Liechtensteinstrasse 41/26, 1090 Vienna, Austria

Future Internet, 2024, vol. 16, issue 5, 1-19

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.

Keywords: skill classification; deep learning; large language models; bipartite skill labels (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/16/5/144/pdf (application/pdf)
https://www.mdpi.com/1999-5903/16/5/144/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:16:y:2024:i:5:p:144-:d:1380839

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().