CCDI Toolkit: Diversity & Inclusion Councils
Bibliographic record
Abstract
Diversity and inclusion is a core leadership competency in today’s organizations. As an inclusive leader, I understand the need and value of diversity of thought. It is well documented that diversity of thought is vital to an organization’s operational success. However, success will not be achieved by diversity alone. Once you have diverse people in the organization, how do you create an inclusive culture? As leaders, we must look at how we can be inclusive to make sure that the benefits of having a diverse workforce contribute to the business success of our organizations. The Global Diversity and Inclusion Benchmark recommends executive-led diversity councils as a foundational structure for an inclusive organization. We are pleased to present the latest in our toolkit series Diversity and Inclusion Councils: Toolkit, which provides insight to having a properly structured and empowered diversity and inclusion council. In this toolkit, the author Sujay Vardhmane discusses two key pillars needed to create inclusive environments: 1. leaders who are committed to diversity and inclusion, and 2. the structures for successful diversity and inclusion councils. This toolkit defines diversity councils; describes the types; explains the value of diversity and inclusion councils to different areas of the organization and provides guidance on operationalizing diversity councils in your organization. It includes references to tools that will help you measure and report the results that will help your organization move ahead of its competition. The biggest takeaway for you the reader is the checklist for a successful diversity and inclusion council. Overall, this toolkit provides a framework that will help you implement a diversity and inclusion council to produce organizational results from an inclusive culture. We hope you enjoy and find value in this toolkit. We look forward to bringing you more tools and resources as we engage dedicated professionals across Canada to solve our biggest inclusion challenges. Thanks. Michael Bach, CCDP/AP Founder and CEO Canadian Centre for Diversity and Inclusion
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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 | 0.014 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.007 | 0.025 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.225 | 0.188 |
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".