Bibliographic record
Abstract
So far ergonomics has been concerned with two categories of activities: correction and design. We propose to add a third category: prospection, and by so doing, we introduce a new series of activities that opens up the future of ergonomics. Corrective ergonomics relates to the past and comes with a demand and a client. It is turned towards the correction of existing situations and aims to reduce or eliminate problems. Here, after delimiting and defining the problem, the challenge is to find the best solution. Ergonomics for design relates to the present and also comes with a demand and a client. It is turned towards the design of new artefacts that have already been identified by a client, and that will allow users to do some activity and attain their goals. Here, after defining the scope of the project and the functional requirements, the challenge is to do the best design. Finally, prospective ergonomics relates to the future and does not come with a demand and a client. It is turned towards the creation of future things that have not been identified yet. Here the challenge is to detect existing user needs or anticipate future ones, and imagine solutions. These three categories of activities overlap and are not exclusive of each other. In this paper we define prospective ergonomics and compare it with corrective ergonomics and ergonomics for design. We describe its origin, goal, and prospects, we analyze its impacts on education and practice, and we emphasize the need of new collaboration between ergonomics and other disciplines.
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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 | 0.019 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".