Is the rise of AI technology scary for HR professionals? Balancing the replacement of employees' skills with AI
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
The researchers used positivism to study the effects of artificial intelligence (AI) applications on human resource (HR) productivity through employee technical expertise. This study targeted employees in the health care sector in Jordan. This research used a self-reported questionnaire as the primary data collection tool. We developed this questionnaire by reviewing relevant literature and designed it electronically using Google Forms. The procedures followed in analyzing the initial research data included a series of procedures employing SPSS and AMOS software. The study results indicate that Artificial intelligence applications (AIA) produce a positive effect on HR productivity challenges (HRP) by interplaying the mediating role of employees' technical expertise (ETE) in the sector of the service industry. HR productivity challenges were the essential purpose of investigating the impact of Artificial intelligence applications through employees' technical expertise to decrease challenges and find a balance between the employees' skills and AI Apps implementation instead of replacing HR skills.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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