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Investigate the relationship between SUN, SSYK and SNI by machine

Reference number
Coordinator Statistiska centralbyrån - Statistiska centralbyrån SCB
Funding from Vinnova SEK 550 000
Project duration November 2022 - December 2023
Status Completed

Important results from the project

Aim and objective of the project is to create a better understanding of how the classifications SNI, SSYK and SUN are connected to each other through machine algorithms and measurements viewed from register populations in the Company Database, Occupational Register) and Education Register within the framework of KLL. We use a correlation measure for this type of data to measure bilateral relationships between two classifications at different levels. The project intends to work to increase the usefulness of the classifications by raising the level of knowledge for stakeholders.

Expected long term effects

In a more automated way, achieve and make visible measures of the relationship between SNI, SUN and SSYK to enable an increase in knowledge about how the relationships between the classifications. Increased level of knowledge about the connections between the classifications at different levels of detail within the labor market and education domains provides good opportunities to better meet the needs of the ecosystem´s stakeholders. At the same time, increased insight can improve interoperability.

Approach and implementation

Involved partners in the project are the Employment Agency, the Swedish School Agency and the Authority for Vocational Schools, as well as SCB (project leading public authority authority). Identified competencies of the participants in the project are project management, methodologists, AI/ML competence, analyst and subject competence. The project was divided into four temporal phases in 2023; Phase 1: Feb-Apr, Phase 2: May-Jun and Phase 3: Sep-Oct - 19 project meetings - 6 main activities - 23 sub-activities

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 16 January 2024

Reference number 2022-02904