The Impact of Individual Differences on Investigative Interviewing Performance
Bibliographic record
Abstract
We examined whether and how individual differences impact investigative interviewing performance by using the Police Interviewing Competencies Inventory (PICI) and the Five Factor Model (FFM) in a two-step research design. In Study 1, the structure of a modified version of the PICI was assessed using a general population sample (N = 300) and a four-dimensional aptitudes scale was created. In Study 2, student participants (N =154) completed the aptitudes and the FFM scales, and then interviewed witnesses who watched a mock robbery crime video. Interviewer performance was assessed based on the amount of details they could elicit, the perception of the witness, and researcher ratings of behaviours and question usage. Three dimensions of the FFM were correlated with the success measures: Agreeableness with witness perception and appropriate questioning, Extraversion with researcher ratings and inappropriate questioning, and Openness with researcher ratings. Only the Communicative-Insisting dimension of the aptitudes scale predicted high researcher ratings. Findings might help police departments to identify potential successful 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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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 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".