Fair Treatment for All: Testing the Predictors of Workplace Inclusion in a Canadian Police Organization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 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 | 0.000 | 0.000 |
| 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 it