A typology of AI-based tasks for the HR function
Why this work is in the frame
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Bibliographic record
Abstract
Purpose This paper aims to elucidate the keys transformations of human resources (HR) tasks amid the age of artificial intelligence (AI). Design/methodology/approach This paper synthesizes recent theoretical and empirical research on the topic of AI and human resource management to establish a typology of AI-based HR tasks. Findings HR jobs will revolve around three types of tasks in the age of AI: mechanical, thinking and feeling. Originality/value AI radically changes HR function and it becomes essential for organizations to clearly define the purpose of using AI, its role and the context of its use in tasks. Strategic value of the HR function will lie in its future reorientation toward feeling tasks. HR managers need to possess the knowledge, skills and abilities to adapt to these tasks and ensure the responsible use of AI.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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