aceUse of Large Language Models (LLM) for Occupational Classification: An example of Early Learning & Child Care (ELCC) Workers
IntroductionThe use of Artificial Intelligence (AI) for occupational classification has drawn the attention of statistical agencies because manual coding is time-consuming and expensive. In Canada, the national "$10-a-day" Early Learning and Child Care (ELCC) program has increased the need for more detailed information on the ELCC workforce. ObjectivesGiven the successful use of Machine Learning (ML) for occupational coding in open-ended Census write-in responses, this study explored the feasibility of creating…
Few-shot LLM classification, paired with expert review, split broad occupation groups into finer subgroups that better identify child care workers than manual coding would have allowed efficiently.
The authors say the LLM tool cannot offer improvements in isolation and must be complemented by subject-matter expert input.
Evidence
- Peer-reviewedInternational Journal of Population Data Science2026-10-19
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Truvace Impact Record TRV-2026-2031, v1: “Use of Large Language Models (LLM) for Occupational Classification: An example of Early Learning & Child Care (ELCC) Workers.” Truvace, 2026-10-20. /record/TRV-2026-2031 (accessed at citation time). sha256 07be0986f81075db…
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