Projecting Grid Compression to Reduce Gender Salary Gaps
Bibliographic record
Abstract
This paper examines the effects of grid compression on gender-based salary gaps. In this case, the workplace setting is a large Canadian research university previously found to have both a positionally segregated cohort of academic staff and a persistent gender-based differential in their salaries. The annual salary grids at this institution underwent seemingly subtle compressions over the course of two decades, with within-rank compression largely confined to the first decade and across-rank compression confined to the second decade. We employ a simulation methodology to check whether the two types of compression reduce the salary gap to varying degrees. After deflating staff salaries back to the start of each decade, we project the salaries forward using the historical annual increases and grids in place in each year at the university. We compute the gap present in the salary distribution at the start and end of each decade in the simulation, and check whether the gap decreased more across one decade than the other. We find that across-rank compression of the institution’s salary grid during the second decade narrows the salary gap to a greater extent than the within-rank compression of the earlier decade. Our work demonstrates that employers using stated salary grids could use simulation to monitor the equity effects of their pay policies and shows that they could accelerate the closure of gaps through consciously altering relationships among pay levels at different points in the grid’s hierarchy.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".