The Effectiveness of Workforce Diversity Management Program in Municipal Government; A Case Study of the City of Mississauga Program
Bibliographic record
Abstract
Public administration currently functions in an era where the traditional methods, teachings as well as processes no longer comport with the existing realities. Factors such as rapid globalization, immigration and integration have contributed to this change. This has resulted in an exponential increase in the number of individuals from foreign countries with diverse backgrounds and cultures working alongside local people within the same organization. This has led to increasing calls for governments and in particular, municipal governments to create a more diverse workforce that is reflective of the population they serve. A popular tool municipal governments including the City of Mississauga have utilized to achieve this is diversity management programs which is the focus of this report. This report employed a mixed methodology in its exploration of “How Effective are Municipalities’ Workforce Diversity Management Programs?” with a particular focus on the City of Mississauga's Workforce Diversity Management Strategy. This was assessed through the lens of the Values Principles Model (VPM) which articulated that for these programs to be effective, they must factor into consideration four key components; representation, participation, application and appreciation. The research revealed that while the City has made progress in its quest to create a more diverse and inclusive work environment. The City has also experienced challenges as it works towards this goal. According to the data regarding representation, there is an overrepresentation of white employees and a significant underrepresentation of visible minorities at the City. Regarding participation, particularly at the leadership and High Potential (HiPo) employees’ levels, the data revealed that visible minorities and women are underrepresented, while white employees and in particular, male employees are significantly represented at both levels of leadership. At the rank-and-file level, employees express a desire to participate in the process. Regarding application, the data revealed there are inconsistencies across divisions, departments, teams and leaders in the application of its diversity strategies. Lastly, regarding appreciation, data were limited in terms of incentives and benefits the City has in place to respond to the diverse needs of its employees.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".