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Record W221290268

Where Vocational Rehabilitation Consumers Work According to the Standard Occupational Classification System

2010· article· en· W221290268 on OpenAlexaboutno aff
Daniel L. Boutin

Bibliographic record

VenueJournal of rehabilitation · 2010
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsnot available
Fundersnot available
KeywordsCensusPopulationGovernment (linguistics)Fiscal yearVocational educationDocumentationPsychologyPolitical scienceDemographySociologyLawComputer sciencePedagogy
DOInot available

Abstract

fetched live from OpenAlex

By incorporating the Standard Occupational Classification (SOC) into case service documentation with the start of the 2007 fiscal year (FY) on October 1, 2006, the Rehabilitation Service Administration's (RSA) vocational rehabilitation (VR) program joined other federal agencies in using a standardized approach to classifying occupational information (Levine & Salmon, 1999; RSA, 2006). As a result, the SOC replaces the outdated Department of Labor's Dictionary of Occupational Titles (DOT) as the formal system to classify the type of occupation achieved by VR consumers (Mariani, 1999; U.S. Department of Labor, 1991). Since the concept of employment is central to many rehabilitation practitioners and people with disabilities (Fraser, Vandergoot, Thomas, & Wagner, 2004; Martz & Xu, 2008), it is important to explore and describe this baseline occupational information for consumers of the VR program and the general United States population. Preceding this analysis is a brief history on classifying occupations followed by an overview of the SOC. Classifying Occupations The first occupational classification system in the United States was developed with the 1850 Census of Population (Levine & Salmon, 1999; Levine, Salmon, & Weinberg, 1999). The government surveyed the population for the profession, occupation, or trade of each person over 15 years of age (U.S. Census Bureau, 2009a, Schedule no. 1 section). From this one question to 23 million residents across 30 states, 322 occupations were identified including cotton-gin maker, drover, lath maker, rag collector, and stevedore (Levine et al., U.S. Census Bureau, 2009c). Eventually, the census surveys included not just one, but multiple questions regarding the nature of work performed (U.S. Census Bureau, 2009b). The U.S. government has since been collecting occupational data on its residents for almost 160 years. Following the rapid expansion of U.S. manufacturing in the early decades of the 20th century, a Standard Industrial Classification (SIC) system was created to systematically organize expanding industries (Levine et al.). An important aspect of the SIC was that it conformed to the existing industrial structure of the United States (Pearce, 2009). For example, industries were classified by a four-digit code and represented agriculture, communication, construction, electric, finance, fisheries, forestry, gas, insurance, manufacturing, mining, real estate, retail, sanitary services, services, transportation, and wholesale trade. The most recent version of the SIC was hierarchically sectioned into divisions (e.g., Division I: Services), major groups (e.g., Major Group 83: Social Services), and industrial groups (e.g., Industrial Group 8331 : Job Training and Vocational Rehabilitation Services) (U.S. Department of Labor, 2009). The SIC was used for many decades until it began to show signs of stress as the nation shifted to a service-oriented economy with the introduction of new industries such as information technology, healthcare, and high-tech manufacturing (Levine et al., 1999). In addition, the 1994 North American Free Trade Agreement (NAFTA) opened up trade with Canada and Mexico, signaling the need for an international industrial classification system. In 1997, the Office of Management and Budget (OMB) replaced the ailing SIC with the North American Industry Classification System (NAICS) (Murphy, 1998). The NAICS is hierarchically organized by groups of industries with similar production processes and can be individualized by participating countries to meet their own needs (Levine et al.). NAICS' six-digit classification codes allows for greater flexibility in structure than the SIC (Murphy). The highest level of aggregation represents industrial sectors (e.g., information), followed by subsectors (e.g., broadcasting and telecommunications), industry groups (e.g., radio and television), international industries (e. …

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.050
GPT teacher head0.453
Teacher spread0.403 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2010
Admission routes1
Has abstractyes

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