Development and Validation of the Marginalized-Group-Focused Diversity Climate Scale: Group Differences and Outcomes
Bibliographic record
Abstract
Abstract In this research, we created and tested the validity of a Marginalized-Group-Focused Diversity Climate Scale (MGF-DCS) following Hinkin’s (1998) best practices. Previously, no measure of diversity climate has been validated. Furthermore, addressing challenges concerning the basis of diversity climate perceptions, we reviewed disparate diversity climate definitions and scales to identify its core components and sources, focusing on the treatment of organizational members who identify as marginalized group members. Using full-time employee samples ( N = 1639), tests of content validity (study 1), exploratory factor analysis (study 2), confirmatory factor analysis (study 3), convergent and discriminant validity (study 4), and criterion validity (study 5) were conducted. Results suggest that the MGF-DCS comprises three subscales: (1) interpersonal valuing of marginalized groups; (2) organizational representation and inclusion of marginalized groups; and (3) organizational anti-discrimination. Furthermore, the MGF-DCS exhibited measurement invariance across marginalized group identification. In study 5, using the MGF-DCS, we tested how perceptions of diversity climate predict organizational and personal outcomes, as moderated by participants’ marginalized group identification. In general, the more participants perceived their workplaces to have a positive diversity climate, the better they saw social dynamics in their workplace (e.g. higher cohesion) and the better their personal outcomes (e.g. lower job stress); in some cases, these benefits were stronger for employees identifying as marginalized group members (e.g. less experienced discrimination). Thus, the MGF-DCS provides a reliable and valid assessment of diversity climate in organizations that can be used to advance theory, research, and diversity management practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".