Lights, camera, persuasion : examining the impacts of impression management tactics on predictive validity
Bibliographic record
Abstract
The study explored the use of self presentation behaviours (i.e., deceptive, and honest impression management; IM) in asynchronous video interviews (AVIs).Applicants use of IM in interviews has the potential to enhance (honest IM) or detract (deceptive IM) the predictive validity of interviews.However, scant research exists on the potential impacts of IM on predictive validity of job interviews.The current study employed a novel approach by collecting data in two stages: first, participants (n = 212) completed a mock AVI; next, participants (n = 168) completed two in-basket HR tasks to capture performance.Results indicated that honest self-promotion had a positive relationship with interview and task performance, and a significant indirect effect.Other IM tactics lead to some mixed and contrary findings.Proposed moderators (experience, age, and anxiety) did not impact results.Overall, honest and deceptive IM demonstrate their importance to both interview and job performance.
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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 | 0.038 | 0.311 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".