Overview of the “Gender & Work” track at the IEA 2021 congress
Bibliographic record
Abstract
BACKGROUND: The International Ergonomics Association (IEA) is an international federation of associations created in 1959, whose mission is to extend the scope of ergonomics research and intervention to all spheres of society in order to improve human well-being. OBJECTIVE: This article presents an overview of the main research papers that were presented at the 21st Triennial IEA 2021 Conference. METHOD: A total of 23 talks, from nine countries, were presented over four sessions. These papers were summarized based on reading the abstracts and taking notes at the time of the oral presentation. RESULTS: The themes of these sessions were: 1) Knowledge Transfer, Gender and Ergonomics 2) Approaching Ergonomic Interventions with a Sex/Gender Lens: Designing Training for Ergonomists 3) Ergonomic Studies of Atypical Work and Vulnerable Population Through a Sex/Gender Lens: Toward Better Understanding of Context and Risks, for Better Prevention and 4) Gender and Occupational Risks (Part 1: Exposure and Risk Perception; Part 2: Strategies to Manage Risk). CONCLUSION: Ergonomists are beginning to understand that they have the qualifications and legitimacy to play a role in reducing workplace health inequities and helping to make workplaces inclusive and rich of all the workers' diversity. The four sessions of the Gender and Ergonomics TC have moved ergonomics practice a step closer to that goal.
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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 | 0.015 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.090 | 0.029 |
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".