Diversity management practices: Comparing Cox and Blakes recommendations to current research and practice
Bibliographic record
Abstract
organizations should benefit. They based these advantages, in part, on organizational practices that were current at the time. In the present paper, we re-visit these advantages and discuss current research as well as organizational ‘best practices ’ with regard to diversity. Through an examination of award winning diversity practices we discuss recommendations for the future of research and practice. In the early nineties, Cox and Blake (1991) detailed six advantages they believed would benefit diverse organizations. These advantages included cost, resource acquisition, creativity, problem solving, systems flexibility, and marketing. Their article has been highly influential in academic circles as evidenced by its continued citation in numerous academic journal articles (e.g., Basset-Jones, 2005), organizational behavior and (e.g., Johns & Saks, 2005) human resources management textbooks (e.g., Gomez-Mejia, Balkin, Cardy, Dimick & Templer, 2004), and books on diversity management (e.g., Schneider & Barsoux, 2003). However, less clear is the impact of the article on organizational diversity practices. The purpose of this paper is to examine both practical recommendations and current organizational practices as they relate to the advantages put forth by Cox and Blake. To provide
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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 | 0.041 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".