Diversio Diversity and Inclusion Survey – Framework and Psychometric properties
Bibliographic record
Abstract
Reliable and valid methods are imperative to assess any organization’s diversity and inclusion practices. Therefore, the Diversio Diversity and Inclusion Survey (DDIS), an instrument built on a framework of five core themes (inclusive culture, fair management, access to networks, flexible working conditions, and safe working environment), and designed to measure inclusion metrics for organizations, was tested to examine its psychometric properties. The DDIS was developed through a collaboration of industry experts, including those with the Canadian Council for Aboriginal Business (CCAB) and the LGBTQ Chamber of Commerce. Initial testing and focus groups with over 60 participants belonging to equity-deserving groups ensured the instrument had good content validity. After the initial testing, pilot testing involving a diverse sample of working adults from 25 companies in Canada, the U.S., and the United Kingdom was completed. Psychometric properties of the 5-item DDIS scale were examined based on a cross-sectional survey of 8,800 working adults from various industries worldwide. The internal consistency reliability of the scale was analyzed using Cronbach’s alpha coefficient1. The Cronbach alpha was 0.840 with all item-total correlations greater than 0.5. Therefore, the DDIS, which has good content validity and good internal consistency, should prove helpful in conducting assessments of diversity and inclusion culture and practices at any organization. In addition, organizations can survey their employees to gather relevant information to drive policy and organizational change.
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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 | 0.023 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".