8290363 The international partnership on automatic job coding (IPAJC): a hub for automatic job and industry coding tools and expertise for occupational health research
Bibliographic record
Abstract
<h3>Objectives</h3> The goal of the International Partnership on Automatic Job Coding (IPAJC) is an interdisciplinary partnership aiming to enable and promote efficient collection, processing, and accessibility of job information through improvement of automatic coding tools. <h3>Methods and Results</h3> Partners within IPAJC create and apply various automatic and semi-automatic coding tool using different methods. Examples include fuzzy matching and Boolean operators (e.g. CASCOT, University of Warwick), machine learning (ML) neural network (e.g. NIOCCS, US NIOSH), ML boosted decision trees (e.g. OccuCoDE, Ludwig Maximilian University, Munich; OPERAS, Utrecht University), ML ensemble classifiers (e.g. SOCcer ,US NCI and AUTONOC, University of New Brunswick/Dalhousie University), and large language models (e.g. TNO-AOC, TNO). Depending on the specific tool, free text job descriptions in different languages are converted into standardised occupation codes such as various versions of international ISCO, Canadian NOC, German KldB, UK SOC, and US SOC. Although most tools thus far focused on coding to standardised occupations, some tools for coding to standardised industries are also available (e.g. CASCOT; NIOCCS; SOCcer CLIPS). <h3>Conclusion</h3> The IPAJC continually seeks partners and job description data for development and validation of current and future automatic job coding tools. Our expertise on survey methods, occupation data collection, and occupation coding is improving job data collection and exposure assessment at scale in occupational health research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".