The diverse organization: Finding gold at the end of the rainbow
Bibliographic record
Abstract
Abstract The breadth and depth of research on organizational diversity reveals the complex nature of diversity in organizations. Indeed, research in the realm of human resource management focuses on diversity applied to a variety of topics, including recruitment, retention, succession planning, and work‐life management, among others. In this article, we use Cox and Blake's (1991) advantages as a framework to review the diversity literature and suggest that organizational culture may be key to understanding when organizations will benefit from a diverse employee base. Specifically, organizations that emphasize inclusion and integrate diversity into all policies and practices may benefit to a greater extent compared with organizations focusing on diversity as a stand‐alone practice. Through an examination of academic research and the award‐winning diversity program of Campbell Soup Company (Catalyst, 2010), we make culturally based propositions to further diversity research in, and the practice of, human resource management. © 2011 Wiley Periodicals, Inc.
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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.036 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".