An introduction to the special issue on digital human modeling (dhm) in Ergonomics 4.0
Bibliographic record
Abstract
[Extract] Welcome to this special issue of the IISE Transactions on Occupational Ergonomics and Human Factors! Our primary motivations in developing this issue were the emerging concepts of Ergonomics 4.0 and 5.0 in Human Factors and Ergonomics within the wider frameworks of Industry 4.0 and 5.0; specifically, clarifying their paradigms and contributing to the understanding of how, and if, digital human modeling plays a role in these concepts. Papers for this special issue mostly originated in the Digital Human Modeling and Simulation (DHMS) track at the International Ergonomics Association (IEA) triennial world congress in Vancouver, Canada (IEA2021). The aim of the DHMS sessions at this congress was to present the latest developments in DHM with a focus on the conference theme, “HFE in a Connected World – L’ergonomie 4.0.” Participants at IEA2021 were able to make shortened submissions to the conference in view of an expression of interest for a full paper submission to this special issue. The IEA DHMS scientific committee members then invited selected authors to make such a submission to this special issue.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.157 | 0.077 |
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".