Human Factors for a Better World: Case Studies in Community Ergonomics
Bibliographic record
Abstract
This panel brings together global experts to explore the evolving practice of community ergonomics—an approach that applies Human Factors and Ergonomics (HFE) to co-design systems with and for marginalized communities. Building on the lessons articulated in Barton et al. (2025), panelists will present diverse case studies that highlight both the promise and complexity of community-engaged HFE work. Panelists will reflect on the epistemological shifts, methodological adaptations, and relational commitments required to move toward genuine collaboration and social impact. Attendees will leave with tangible strategies for engaging communities as co-designers, as well as deeper insight into how our assumptions as HFE professionals shape the outcomes of our work. This session is a call to action for expanding the scope, values, and practice of ergonomics for a more equitable world.
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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 | 0.045 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.031 | 0.014 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".