Inclusion starts with “I”? The missing ingredient in leading change: the case of the Royal Canadian Mounted Police (RCMP)
Bibliographic record
Abstract
Purpose This paper examines how organizations can overcome cultural barriers and support leaders in creating more inclusive workplaces. Design/methodology/approach Drawing from personal experience as a senior leader within the Royal Canadian Mounted Police (RCMP), the author provides a brief overview of the organization’s approach to creating a more diverse and inclusive workplace, including her role in overseeing the change effort. The author then describes how certain aspects of the RCMP culture manifested in bias against others, and contributed to leaders’ efforts to cover up important parts of their identity to fit in. Finally, the author presents self-acceptance and personal vulnerability as building blocks for a more inclusive style of leadership. Findings The findings of this paper suggest that diversity and inclusion efforts that fail to address harmful aspects of organizational culture are unlikely to be successful. The findings also suggest that this barrier may be overcome through a greater understanding of the cultural norms that are most valued, of practicing inclusion at three different levels, starting with the individual, and of supporting leaders to begin the practice of inclusion, staring from the inside out. Originality/value This paper makes an important contribution to the field of organization development by providing a brief snapshot of one leader’s experience in attempting to create a more diverse and inclusive workplace, and makes recommendations for how the challenges presented might be overcome.
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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.096 | 0.043 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".