Effective Human Resource Management: A Global Analysis
Bibliographic record
Abstract
Effective Human Resource Management is the Center for Effective Organizations' (CEO) sixth report of a fifteen-year study of HR management in today's organizations. The only long-term analysis of its kind, this book compares the findings from CEO's earlier studies to new data collected in 2010. Edward E. Lawler III and John W. Boudreau measure how HR management is changing, paying particular attention to what creates a successful HR function-one that contributes to a strategic partnership and overall organizational effectiveness. Moreover, the book identifies best practices in areas such as the design of the HR organization and HR metrics. It clearly points out how the HR function can and should change to meet the future demands of a global and dynamic labor market. For the first time, the study features comparisons between U.S.-based firms and companies in China, Canada, Australia, the United Kingdom, and other European countries. With this new analysis, organizations can measure their HR organization against a worldwide sample, assessing their positioning in the global marketplace, while creating an international standard for HR management. Note on electronic editions: This book contains large tables that may not display clearly on a small screen. To easily read some of the tables, you may wish to use the desktop version of your selected reading system.BV_PDF BV_EPUB
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".