Impression Management and Faking in Selection: Prevention, Promotion and Consequences
Bibliographic record
Abstract
This symposium brings together four innovative studies centered on impression management (IM) and faking, conducted by researchers from four different institutions. Using various methods (e.g., experiments, intervention, and surveys), three of the studies investigate novel approaches to influencing applicant behavior in various assessment tools including game-based assessments (GBA), personality tests, and job interviews. In addition, the symposium addresses the potential long-term consequences of IM tactics and concludes with an engaging discussion that expands on the insights from the presented papers, offering a broader perspective on the topic. Game On, Faking Off? Comparing Personality Game-Based Assessments to Traditional Personality Tests Author: Marie Ohlms; University of Freiburg Author: Ard Barends; Leiden University Author: Uwe Pter Kanning; Osnabrück University of Applied Sciences Exploring a Visual Dual-Task for Faking Prevention on Personality Assessments Author: Sabah Rasheed; Wilfrid Laurier University Author: Chet Robie; Wilfrid Laurier University Investigating the Effect of Honest Impression Management Training Author: Johanna Bayón; University of Zurich Author: Martin Kleinmann; University of Zurich Author: Anna Luca Heimann; University of Zurich Interview Impression Management and Workplace Outcomes Author: Reegan Prete; Author: Nicolas Roulin; Saint Mary's University
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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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".