Who Is Conducting "Better" Employment Interviews? Antecedents of Structured Interview Components Use
Bibliographic record
Abstract
The employment interview remains a unique paradox. One the one hand, decades of research demonstrates that using more structured components (e.g., question consistency, evaluation standardization) can largely improve the psychometric properties of interviews. On the other hand, although interviews are almost universally used, many interviewers still resist using structured formats. We examined the use of seven structure components by 131 professional interviewers, and their association with three types of antecedents: interviewers’ background (e.g., experience, training), the focus of the interview (selection vs. recruitment), and interviewers’ personality (based on the HEXACO model). Interviewers’ background (i.e., training) and the focus of the interview were largely associated with the use of question sophistication, question consistency, note-taking, or evaluation standardization. Personality (i.e., extraversion) was mostly associated with rapport-building or probing. Our findings highlight the importance of providing formal training to interviewers, but suggest that attempting to eliminate less-structured components could encounter resistance from some interviewers.
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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 | 0.152 | 0.396 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".