Bibliographic record
Abstract
Strategic Management of Diversity in the Workplace discusses the strategic management of ethnic and cultural diversity by taking particular examples from Australia, Canada, The United Kingdom and the United States of America, in order to determine the salient benefits that organisations could derive when ethnic and cultural differences are seen as opportunities, not as problems, and are viewed as benefits rather than threats. Strategic Management of Diversity in the Workplace provides a clear demonstration of the benefits, conflicts and challenges faced by organisations. The renewed interest in multiculturalism in academic and policy circles revives the debate about issues related to the management of ethnic diversity in society at large and in specific settings, such as corporate Australia. This book specifically focuses on this problematic area by aiming to explore the practice of management and application of multiculturalism in the workplace. This book seeks to examine post-multiculturalism in Australia and explore whether it has affected the ways in which corporate Australia deals with issues of diversity and the lessons learned here are ones that apply across the business world. Strategic Management of Diversity in the Workplace would be of interest for researchers, academics, undergraduate and postgraduate business degrees students in the fields of Strategic Human Resources Management, Cross-Cultural Management, Managing Workplace Training and Managing and Leading People.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".