Past, present, and future of E/HF for sustainability: A perspective from the HFSD Technical Committee
Bibliographic record
Abstract
BACKGROUND: Sustainability is a highly important theme worldwide and currently is being tackled by almost all disciplines. Indeed, the future of humanity is dependent on the actions taken now and in the immediate future. The Ergonomics and Human Factors (E/HF) community has not been indifferent to this issue, and one of the concrete actions adopted by the International Ergonomics Association (IEA) was the establishment of the "Human Factors for Sustainable Development" (HFSD) Technical Committee. OBJECTIVE: To identify future paths of action, this paper recognizes the trajectory of the HFSD Technical Committee, summarizes the contributions presented at IEA2021, the International Congress on Ergonomics held virtually in Vancouver in 2021, and reflects on key aspects that should be boosted by the Technical Committee. METHODS: This is a qualitative interpretative study that reflects on the contributions of members of the HFSD community working on E/HF for sustainability. RESULTS: Central topics and opportunities in E/HF and sustainability include complexity of systems, behaviors, and work; energy use and consumption; co-design, interconnectivity, territories, and the relationships with stakeholders. CONCLUSION: Although the contributions have been growing, there is still a lot of work needed, both theoretically and practically. Themes to be discussed include the concepts of sustainability and work. Considering the centrality of human beings (i.e., decision making for achieving the different dimensions of sustainability), the authors identify a set of values as core principles for leading the discussion.
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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 | 0.040 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.021 | 0.020 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".