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
Employee promotion plays a crucial rule in human resource management, yet former promotion systems often suffer from inefficiency, inconsistency and bias. This study aims to develop a prediction system based on machine learning, using a Kaggle HR dataset of 54,808 employees. This study integrates logistic regression, random forest, XGBoost and gradient boosting models. To address class imbalance where only 8.5% of employees were promoted, this article uses oversampling techniques to enhance model performance. The result shows that Logistic Regression achieved the best baseline recall of 0.7112, while Random Forest with oversampling reached a recall of 0.9466. Key predictive features include training scores, previous-year ratings and departmental affiliations. The system implies how comprehensive and balanced feature integration can improve fairness and accuracy in promotion decisions. For companies like JMD company, the framework is universal because it is data-driven and transparent. A replicable model for industries seeking digital improvement in employee promotion systems has been created.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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