Barriers to Advancement: The Impact of Gender, Age, and Regional Bias on Promotion Decisions
Bibliographic record
Abstract
Our study presents a machine learning approach to predicting employee promotions in a large organization using comprehensive HR data. Utilizing a dataset of over 50,000 employee records and thirteen key features—including demographics, length of service, performance ratings, and training metrics—the research implements systematic data preprocessing, imputation for missing values, outlier detection, and robust feature engineering. Ensemble models such as AdaBoost, Gradient Boosting, Random Forest, and XGBoost were compared using F1-score as the primary evaluation metric, with special strategies to address class imbalance, including SMOTE and random undersampling. Through rigorous cross-validation and hyperparameter tuning, the AdaBoost model trained on the original data emerged as the best performer, achieving an F1-score of 0.73 on the validation set. Feature importance analysis revealed that recent performance, awards, and training scores are the strongest predictors of promotion. These findings demonstrate the potential of machine learning to improve fairness, consistency, and transparency in HR promotion decisions.
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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 | 0.014 | 0.042 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".