Investigating deception in second language speakers: Interviewee and assessor perspectives
Bibliographic record
Abstract
Purpose The first of two experiments investigated the effect that speaking in a non‐native language has on interviewees’ perceptions of their interview experience. A second experiment investigated evaluators’ perceptions of the credibility of interviewees who spoke in their native or non‐native language. Method For the first experiment, 52 participants told the truth or lied about their identity during a mock border control interview. All of the participants were interviewed in English, for half of the sample this was their native language, and for the other half of the sample English was not their native tongue. Post‐interview, all participants completed a self‐report questionnaire relating to their perceptions of their interview experience. For the second experiment, 128 participants evaluated the credibility of interviewees from the first experiment. The modality of presentation of interview clips was varied and included ‘Visual and Audio’, ‘Visual Only’, ‘Audio Only’, and ‘Transcript Only’. Results Non‐native speakers were more likely than native speakers to report being nervous and cognitively challenged during their interviews and were more likely to monitor their own behaviour. Overall, evaluators were better able to distinguish between truth tellers and liars who were speaking in their native language than between truth tellers and liars who were non‐native speakers. Relative to native speakers, there was a smaller truth bias for evaluations of non‐native speakers. When evaluators were considering the non‐native speakers, they achieved higher discrimination accuracy when they were exposed to ‘Visual Only’ or ‘Transcript Only’ presentations than when they were shown the ‘Visual and Audio’ or ‘Audio Only’ interview clips. Conclusions Self‐reported experiences of a mock border control interview differed dependent on whether interviewees were speaking in their native or non‐native language. Discrimination accuracy was better for native speakers than it was for non‐native speakers and was at its worst when evaluators heard the accents of the non‐native speakers.
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How this classification was reachedexpand
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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".