Observers' Language Proficiencies and the Detection of Non‐native Speakers' Deception
Bibliographic record
Abstract
Summary We examined whether observers' language proficiencies affected their abilities to detect native and non‐native speakers' deception. Native and non‐native English speakers were videotaped as they either lied or told the truth about having cheated on a test. A total of 284 laypersons—who were either native or non‐native English speakers themselves—viewed these videos and indicated whether they believed that the speakers were being truthful or deceptive. Observers were more accurate when judging native speakers than when judging non‐native speakers, suggesting that perceptual fluency aided deception detection. Although there was no effect of observers' language proficiencies on discrimination, their belief that interviewees were telling the truth increased with proficiency. On the whole, these findings suggest that non‐native speakers may be at greater risk of being incorrectly classified in forensic contexts.Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.001 | 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".