The Influence of Artificial Intelligence on Human Resources Management Processes
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
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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