Analyzing the Gender Wage-gap in Ontario's Public Sector
Bibliographic record
Abstract
The disparity in wages between men and women is a well known fact; however, the contribution of each known factor is not fully understood. Leveraging the salary information provided by the Ontario Ministry of Finance could allow for a better understanding of the factors that contribute to gender wage disparity. The Ontario public salary data, also known as the 'Sunshine List', contains the salary information of individuals working in the public sector that earn $100,000 or more annually. Unfortunately, the Sunshine List data is not in a form that allows for direct analysis. The information must first be collected, cleaned, and standardized due to formatting inconsistencies within the Sunshine List data. Furthermore, although these salaries are publicly available, a key attribute is missing from the public data, the gender variable. A novel hybrid model is proposed to predict the gender based on an individual's given name, and the original database is augmented with the new gender variable. With the new database created, the wage-gap is analyzed and the results are presented and discussed. The findings of this research are being used by Ontario's provincial government to reassess and change current policies for pay equity.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".