The Influence of Artificial Intelligence on Human Resources Management Processes
Bibliographic record
Abstract
Welcome/Introduction Paper presentations • Impact of Natural Language Processing on Personnel Selection. Presented by Emily Campion and Michael Campion • Artificial Intelligence and Performance Management. Presented by Arup Varma • Artificial Intelligence, Algorithms, and Compensation Practices and Decisions: Challenges and Opportunities. Presented by Janet Marler • Will AI Make Radically Changes to Human Resource Management Processes? Presented by Kimberly Lukaszewski Discussant, Gary Latham Impact of Natural Language Processing on Personnel Selection Author: Emily D. Campion; U. of Iowa Author: Michael A Campion; Purdue U. Artificial Intelligence and Performance Management Author: Arup Varma; Loyola U. Chicago Author: Vijay Edward Pereira; NEOMA Business School Author: Parth Patel; Australian Institute of Business AI, Algorithms, Compensation Practices and Decisions: Challenges and Opportunities Author: Janet H. Marler; U. at Albany, State U. of New York Will Artificial Intelligence Make Radically Changes to Human Resource Management Processes? Author: Kimberly Lukaszewski; Wright State U. Author: Dianna L. Stone; U. of New Mexico, Albany, and Virginia Tech Author: Richard Johnson; Washington State U.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".