Extinguishing injustice: growing equity, diversity and inclusion in Canadian fire departments
Bibliographic record
Abstract
Purpose This paper evaluates the value and necessity of greater equity, diversity and inclusion (EDI) in Canadian fire departments. Rather than focussing on changing hiring practices, the paper seeks to highlight how leadership can implement a culture of EDI that will encourage all people to participate. Design/methodology/approach From a leadership perspective, this paper aims to show how EDI can improve firefighter teamwork and job performance whilst satisfying moral obligations to better represent Canadian communities. Strategies and their limitations for communication and culture change are discussed. Findings Leaders of Canadian fire departments can utilise organisational change models focussing on improved communication techniques and models to implement cultural changes needed to allow for more EDI. Specific recommendations based on business research into culture change, communication and EDI are outlined. Practical implications Recommendations to fire department leadership for cultural changes and communication are provided. Further, strategies and reasoning for why inclusive departments are more effective are given. Social implications Creating a more inclusive culture in fire departments will lead to an increase in applications from people who have not typically applied in the past. Originality/value There has been little research or recommendations on increasing EDI in Canadian fire departments through cultural changes. Most existing literature is vague and tends to focus on hiring practices over an analysis of internal culture. This article provides analysis of best business practices and applies those to the cultural context of fire departments to promote culture 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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".