Artificial Intelligence enabled Employee Performance Prediction using Comprehensive Learning Metrics
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
Human capital is crucial to the success of businesses in the new knowledge economy. There is a compelling financial justification for developing a system to assess and enhance employee performance, making performance measurement at the level of the individual worker a must. With Data Mining, HR professionals may find new and relevant information (HRM). The success of an employee is not dependent on a single trait. Humans vary greatly in their innate talents and the traits that define their personalities. Hence, data from many sectors must be collected for performance monitoring. The purpose of this article is to present an overall grade of an employee's performance taking into account a number of criteria. We begin by contrasting many popular Machine Learning algorithmsagainst one another. We then recommend an ensemble method that draws from the aforementioned methods. The empirical findings show that the suggested model outperforms the state-of-the-art models in terms of precision, recall, F1-score, and accuracy, as compared to the more conventional models.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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