Looking at the What, How, and Why at Various Stages of the Personnel Selection Process
Bibliographic record
Abstract
Personnel selection uses various tools such as interviews, personality tests, and resumes to identify the best applicant for an open position. While there is vast knowledge on the validity of and reactions to these tools, research tends to overlook the what, how, and why of information sharing and reactions. This symposium features five studies examining the processes involved in various stages of the selection. They investigate the cognitive processes that occur during the interview, applicant reactions to different types and formats of questions, and impression management behavior of applicants and recruiters. Thereby, they address influences both by characteristics of the tools and the applicants. Collectively, these papers help advance our understanding of some of the processes underlying commonly used selection tools, which can help refine theories for science and retrieve more specific contributions for the practice of modern personnel selection. Effect of Stimulus- and Response Format on Applicant Reactions Author: Valerie Sophie Schröder; U. of Zurich Author: Pia Ingold; U. of Copenhagen Author: Anna Luca Heimann; U. of Zurich Author: Martin Kleinmann; U. of Zurich Are Traditional Interviews More Prone to Effects of Impression Management than Structured Interviews Author: Benedikt Bill; Ulm U. Author: Klaus Melchers; Ulm U. Gender Differences in Effectiveness of Self-promotion in Cover Letters and Resumes Author: Simonne Mastrella; U. of Guelph Author: Rahul Patel; U. of Guelph Author: Deborah M. Powell; U. of Guelph Assessing and Predicting Maximum and Typical Performance With Job Interviews Author: Johanna Bayón; U. of Zurich Author: Anna Luca Heimann; U. of Zurich Author: Martin Kleinmann; U. of Zurich Introducing the Interviewer Impression Management Scale: Development and Validation Author: Nathalie Von Rooy; U. of Zurich Author: Annika Wilhelmy; U. of Zurich Author: Martin Kleinmann; U. of Zurich Author: Nicolas Roulin; Saint Mary's 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 | 0.026 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".