“I (might be) just that good”: Honest and deceptive impression management in employment interviews
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
Abstract Applicant use of impression management (IM) tactics plays a central role in employment interviews. IM includes behaviors intended to create an impression of competence and likability, and avoid negative impressions. Applicants can influence interviewers’ impressions using both honest and deceptive IM, but measurement of IM has yet to distinguish these two constructs. The goal of the present research was to develop a self‐report Honest Interview Impression Management (HIIM) measure and use this to investigate differential antecedents and consequences of honest and deceptive IM. We report the results of five independent studies (total N = 1,470 interviewees). Studies 1–3 detail the creation of a self‐report measure of honest IM. Studies 4 and 5 utilize this measure to understand the relations between honest and deceptive IM, and their antecedents and consequences. Results demonstrate that honest and deceptive IM are positively related but distinct constructs that have unique antecedents (i.e., age, individual differences, attitudes, situational, and target characteristics) and differentially impact interview outcomes and ratings. Finally, we present a short measure of honest and deceptive IM to be used for time‐sensitive data collection.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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