Fitting the Human: Introduction to Ergonomics/Human Factors Engineering, Eighth Edition
Bibliographic record
Abstract
The aim of this book is to provide “human engineering” for workplaces, tools, machinery, computers, shift work, lighting, sound, climate, work demands, offices, vehicles, healthcare, and the home – and everything else that we can produce – to suit the human body and mind. Now being published in its eighth edition, Fitting the Human focuses on the primary ergonomic task of accommodating the human with the appropriate selection of equipment and tools, work requirements and procedures, physical and social conditions at work, and working hours and shift arrangements. This book provides the ergonomic information needed for the user-friendly design of tasks, equipment, and workplaces. It follows the successful format of previous editions, with updated information and practical guidelines that augment the previous information. It offers refreshed information on ergonomic design for the home and workplace, contemporary ways of working, healthcare and medicine, and artificial intelligence and autonomy. This text also recognizes that cultural differences in living and working vary around the world, so additional insights are offered into ergonomics in global cultures and regions. This title will help the reader understand how to plan and design an overall system and its details to fit the human. Published under the mantra of “solid information, easy to read, easy to understand, easy to apply,” Fitting the Human is written for students and professionals in ergonomics, human factors, product and work design, safety, architecture, management, and all fields of engineering.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.083 | 0.062 |
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".