Occupations, like many other social systems, are hierarchical. They evolve with other elements within the work ecosystem including technology and skills. This paper investigates the relationships among these elements using an approach that combines network theory and modular systems theory. A new method of using work related data to build occupation networks and theorize occupation evolution is proposed. Using this technique, structural properties of occupations are discovered by way of community detection on a knowledge network built from labor statistics, based on more than 900 occupations and 18,000 tasks. The occupation networks are compared across the work ecosystem as well as over time to understand the interdependencies between task components and the coevolution of occupation, tasks, technology, and skills. In addition, a set of conjectures are articulated based on the observations made from occupation structure comparison and change over time.
The Coevolution of Tasks and Technologies
As work changes, so does technology. The two coevolve as part of a work ecosystem. This paper suggests a way of plotting this coevolution by comparing the embeddings - high dimensional vector representations - of textual descriptions of tasks, occupations and technologies. Tight coupling between tasks and technologies - measured by the distances between vectors - are shown to be associated with high task importance. Moreover, tasks that are more prototypical in an occupation are more important. These conclusions were reached through an analysis of the 2020 data release of The Occupational Information Network (O*NET) from the U.S. Department of Labor on 967 occupations and 19,533 tasks. One occupation, journalism, is analyzed in depth, and conjectures are formed related to the ways technologies and tasks evolve through both design and exaptation.
- Publication Date:
- NSF-PAR ID:
- 10334734
- Journal Name:
- Academy of Management Annual Meeting
- Sponsoring Org:
- National Science Foundation
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