Visualization of Latent Components Assessed in O*Net Occupations (VOLCANO): A robust method for standardized conversion of occupational labels to scale ratio format
Bibliographic record
Abstract
Occupations are typically characterized in nominal form, a format that limits options for hypothesis testing and data analysis. We drew upon ratings of knowledge, skills, and abilities for 966 occupations listed in the US Department of Labor’s Occupational Classification Network (O*NET) database to create an accessible, standardized multidimensional space in which occupations can be quantitatively localized and compared. Principal components analysis revealed that the occupation space comprises three main dimensions that correspond to 1) the required amount of education and training, 2) the degree to which an occupation falls within a science, technology, engineering, and mathematics (STEM) discipline versus social sciences and humanities, and 3) whether occupations are more mathematically or health-related. Additional occupational spaces reflecting cognitive versus labour-oriented categories were created for finer grained characterization of dimensions within occupational sets defined by higher or lower required educational preparation. Data-driven groupings of related occupations were obtained with hierarchical cluster analysis (HCA). Proof-of-principle was demonstrated with a real-world dataset (470 participants from the Nathan Kline Institute – Rockland Sample; NKI-RS), whereby verbal and non-verbal abilities—as assessed by standardized testing—were related to the STEM versus social sciences and humanities dimension. Visualization of Latent Components Assessed in O*Net Occupations (VOLCANO) is provided to the research community as a freely accessible tool, along with a shiny app for users to extract quantitative scores along the relevant dimensions. VOLCANO brings much-needed standardization to unwieldy occupational data. Moreover, it can be used to create new occupational spaces customized to specific research domains.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.010 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".