Belonging in the workplace: Methodology for fair and equitable data analysis
Bibliographic record
Abstract
To remain globally competitive, the Canadian mining industry requires sustainability protocols to enhance the hiring and retention of diverse and underrepresented employees. Belonging in the workplace acts as a bridge, but literature demonstrates bias in current survey analysis practices that reinforces status quo and favors homogeneous groups. Using mediation analysis, this research investigated how an employee’s intersections of identity (gender, ethnicity, and career level) influence belonging in the workplace perception. Data from 3,508 participants from 13 Toronto Stock Exchange listed companies were used to evaluate perceived organizational belonging through five validated indicators (comfort, connection, contribution, psychological safety, and well-being). Using multiplicative analysis, we explored how employees’ intersecting identities change their perception of belonging in the workplace. Study results show clear direct and indirect effects when intersections of identity are accounted for. With the intersections of identity frequently misunderstood in survey analysis and the workplace, this research explores how status quo decisions lead to exclusion and turnover of underrepresented employees. Applying mediation analysis explains the variance in perception of belonging in the workplace and provides insight into the distortions of workplace experience while providing support for sustainability protocols.
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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 | 0.337 | 0.523 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.021 | 0.026 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".