Encyclopedia of human resources management By S.Johnstone, J. K.Rodriguez and A.Wilkinson, London: Edward Elgar. 2023
Bibliographic record
Abstract
Over recent years, there have been many changes in the labour market, in working conditions, in work organization and in business management. Also, technological changes have been major, artificial intelligence and digitalization being only a few of the phenomena observed. This book is really an encyclopedia, in the sense that it covers many themes with short texts of one page or so, with a few references and cross-references in the book itself, to make it possible to go further on a specific issue. The many changes in the world of work, human resources management, or ‘people’ management as many call it, is questioned and will need to change as well. In such a context of organizational and technological change, this 425-page book offers many insights on the various transformations and their impacts. To our knowledge, this is the first encyclopedia to cover themes that go beyond the field of human resources management, or ‘people’ management per se, to include some terms and expressions that could also be seen more fitting in the areas of sociology of work or labour economics. For example, themes such as work organization, working time, work-life balance, precarious employment, teamwork or telework, to name only a few, are important themes in the sociology of work as well as in human resources management. This book is actually the second and updated edition of the Encyclopedia and includes basic definitions and information on key concepts and terminologies, but also some less known or less familiar aspects of HR terminology, as well as some technical expressions that will be useful for practitioners. Some entries also cover the context of human resources management, including issues such as underemployment, online learning, on-the-job learning, onboarding, part-time work, institutional framework, organizational culture, organizational climate, job security, the 4-day workweek or maternity, paternity and parental leave. In the field of innovation or technology, the most recent changes here are taken into account, with digitalization, platform work, big data, digital work, E-human resources management, E-learning, artificial intelligence as well as artificial intelligence human resources management, giving us a very complete picture of ongoing changes and their impact on work and people. Some new concepts such as Green human resources management are also given some light, while more traditional aspects of RHM or even Industrial Relations such as grievance procedures, fixed-term contracts, pensions, peer appraisal, performance appraisal, performance-related pay, fire and rehire, as well as many others are presented. There are also some elements related to theories, for example Theory X or Y, scientific management, psychometric testing and many others. The book contains very concise entries, but each one of them includes a few references (5−10 or so) in order to indicate further reading which can be useful to better understand the concept. There are over 400 entries relating to human resources management, or ‘people’ management field, indicating the comprehensive nature of the book. It is of course difficult to synthesize an Encyclopedia, but this book definitely merits being on the bookshelves of students and teachers in HRM and social sciences, but also in those of managers, union representatives and all who have an interest in the evolution of the world of work, and the transformation of jobs and work organization. In short, this book is very comprehensive and thoughtful and such a reference resource was clearly missing in the field of HRM or people management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.310 | 0.272 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".