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
Objectives 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. Methods and Results 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). Conclusion 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.267 | 0.246 |
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".