Prospective ergonomics: origin, goal, and prospects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it