Who Is Conducting "Better" Employment Interviews? Antecedents of Structured Interview Components Use
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
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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