Does Design Matter? The (Limited) Effects of Six Asynchronous Video Interview Design Features on Impression Management, Reactions, and Evaluations
Bibliographic record
Abstract
ABSTRACT Asynchronous video interviews can use many configurations of design features to create the interviewee experience, but not all designs are equal. Design features may influence interviewees' deceptive and honest impression management, their reactions to the procedure, and interview performance evaluations. Three experiments using mock interviews tested the effects of preparation time and self‐views ( N = 206, from Prolific), reviewing and re‐recording ( N = 230, from Prolific), and giving faking warnings with human versus automated evaluation ( N = 297 university students) on interview outcomes. The design had limited effects on interviewee behavior, but some features may increase interviewees' willingness to fake when used in combination. Opportunities for longer preparation time and re‐recording increased interview performance ratings. Warnings and evaluator type did not affect behavior, reactions, or performance. The implications of these effects are discussed.
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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 | 0.030 | 0.153 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".