Barriers to Advancement: The Impact of Gender, Age, and Regional Bias on Promotion Decisions
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
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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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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