Creating a Culture of Inclusion through a Diversity, Equity and Inclusion Lens
Bibliographic record
Abstract
The Financial Regulatory Agency of Canada (FRAC, a pseudonym) needs to create a more inclusive organizational culture, which will be a significant institutional change. The FRAC is embarking on a cultural transformation, with a primary focus on creating a diversity, equity, and inclusion (DEI) strategy. The Problem of Practice addressed here is the lack of consistent inclusive leadership practices in the FRAC. This Organizational Improvement Plan uses transformational leadership and inclusive leadership principles to garner support and momentum from all organizational levels to achieve the new organizational state. Four frames were used as a tactical tool to analyze the FRAC and pinpoint the choices and actions the organization could make during the improvement process. A political, economic, social, technological, legal, and environmental analysis was done to better understand the forces affecting this change, and a readiness assessment confirmed the FRAC’s tolerance for the proposed change initiative. Following an organizational study, the change path model was used to guide the change process. The financial, administrative, and human resource implications of three potential responses to the Problem of Practice were identified. The creation of a DEI strategy focused on integrating an inclusion leadership competency was selected as the best course of action for sustainable change. The plan, do, study, act methodology was chosen to track and analyze the advancement of DEI at the FRAC. Finally, an implementation plan, communication strategy, and monitoring and evaluation plan were developed, all of which are aligned with the change path model to ensure a cohesive Organizational Improvement Plan.\nKeywords: culture, diversity, inclusion, transformational, inclusive, leadership
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 | 0.019 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.039 | 0.069 |
| Scholarly communication | 0.032 | 0.012 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".