Machine Learning and Human Resource Management: A Path to Efficient Workforce Management
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
In order to achieve effective workforce management, this empirical study investigates the incorporation of machine learning into human resource management (HRM). HRM is a fundamental function that oversees talent acquisition, employee welfare, and performance optimization in organizations. The dynamic nature of today's workplace presents special opportunities as well as challenges for HRM. Machine learning, a branch of artificial intelligence, has the potential to completely transform human resource management (HRM) by means of the use of data-driven decision-making, bias mitigation, employee experience personalization, as well as procedure optimization. The first section of the paper provides an overview of machine learning's application to HRM, with a particular focus on forward-thinking employee turnover prediction, personalized onboarding and training, recruitment automation, in addition to predictive analytics for employee success. Machine learning promotes fairness and equal opportunities by utilizing objective data to address bias in HR procedures. There are numerous advantages to incorporating machine learning into HRM, such as objectivity, personalization, automation that reduces costs, and decision-making based on information. The practical advantages of integrating machine learning in HRM are demonstrated by real-world case studies from businesses like Hilton, Xerox, and IBM. The resulting advantages include improved productivity, lower attrition, and higher employee engagement.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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