Fair Treatment for All: Testing the Predictors of Workplace Inclusion in a Canadian Police Organization
Bibliographic record
Abstract
The growing diversification of the workforce demands that organizational leaders create workplaces in which individuals have a sense of belonging and are valued for their unique contributions. However, beyond the contributions of certain types of leadership, there is insufficient understanding of the factors that impact experiences of workplace inclusion. Using survey data collected from a Canadian police organization ( N = 488) in the spring of 2018, this study examined whether organizational justice (i.e., fair treatment) was positively associated with workplace inclusion, and whether psychological safety mediated the justice–inclusion relationship. The results of structural equation modelling (SEM) revealed that organizational justice was significantly related to inclusion. Organizational justice was also found to indirectly influence perceptions of inclusion, through psychological safety. In other words, when people were treated fairly, they were more likely to indicate their workplace was psychologically safe, which in turn contributed to feelings of inclusion. Finally, the study findings indicated that personal characteristics, including gender, race and occupational role influenced individual experiences of 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".