Belonging in the workplace: Methodology for fair and equitable data analysis
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
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.
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 | 0.005 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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