Prospective ergonomics for the design of future things
Bibliographic record
Abstract
This position paper gives an overview of the field of Prospective Ergonomics (PE) for the design of future products, services, processes, and systems. It presents its definition, links with innovation, characteristics, origin, history, goal, supports, and methods to help people imagine the future. In recent years, PE has been rediscovered, actualised and repositioned to give it greater scope and visibility, and stimulate research, training and professional practice in ergonomics for the design of future things. PE is an ergonomic intervention mode, on a par with Corrective ergonomics and Design ergonomics, upstream of design projects, where the ergonomist is proactive in anticipating or constructing future user needs and in creating future artefacts. PE is supported by four main fields of study: Ergonomics, Prospective, Future-Oriented Cognition, and Creativity, which ensures it is guided by three key principles: human-centred, future-oriented, and focused on creativity for innovation. PE expands the scope of ergonomics by going beyond the design of interactions with artefacts and including the creation of these artefacts in the first place. This has major impacts on research, training, and professional practice in ergonomics because we need new knowledge to work efficiently on the future, training should be enhanced in prospective, innovation, and new product development, and the professional practice is enriched by a new set of activities. In the conclusion we propose future research directions to pursue the development of PE.
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.
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".