Investigating deception in second language speakers: Interviewee and assessor perspectives
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».