The critical role of research in diversity training: how research contributes to an evidence-based approach to diversity training
Bibliographic record
Abstract
Purpose The purpose of the paper is to outline a diversity training framework in which research literatures and findings in psychology and human resource management (HRM) are used to guide organizations in the delivery of diversity training. The author proposes improvements to the current state of diversity training practices and implementations within organizations through the use and integration of research literature. Design/methodology/approach The paper is both a conceptual and a general review paper. It involves the discussion of research on diversity training, as well as diversity and training separately (conceptual), and includes a general analysis of diversity training (review). Findings The paper offers a general review about how psychological and HRM research findings can help organizations better implement diversity training. It suggests that successful diversity training involves a three-part approach: follow established psychological theory to guide selection of diversity training initiatives, use a framework for HR diversity management and adopt practical steps to better manage diversity initiatives (paying careful attention to a needs assessment, linking diversity strategy to business results and establishing metrics and evaluating effectiveness). Practical implications Diversity training has not been and continues to not be research- or evidence-based. This paper outlines some suggestions for integrating psychological and HRM research findings into the delivery of diversity training. The practical implication is that organizations and stakeholders will use a more evidence-based approach to diversity training. Originality/value This paper meets the needs of organizations seeking a more research- and evidence-based approach to diversity training.
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 | 0.520 | 0.607 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.026 | 0.017 |
| Science and technology studies | 0.016 | 0.091 |
| Scholarly communication | 0.059 | 0.072 |
| Open science | 0.009 | 0.039 |
| Research integrity | 0.026 | 0.034 |
| Insufficient payload (model declined to judge) | 0.006 | 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".