When the eyewitness to a crime is an English language learner: Identifying and resolving troubles in understanding in interviews
Bibliographic record
Abstract
Abstract Investigative interviewing can be a difficult task. Challenges may be exacerbated when interviewing English language learners. The field of eyewitness testimony lacks research on what troubles of understanding are generated by non‐native speakers and how these troubles are resolved. Thus, we undertook an exploratory study in which we aimed to provide definitions for identifying and characterising both troubles of understanding and resolutions. Data were simulated interviews between 17 dyads of native English‐speaking student‐interviewers and English language learner witnesses of a mock crime. We identified misunderstandings in each interaction and tracked who misunderstood, when the misunderstanding occurred (free/cued recall), whether it was resolved, and resolution strategies. We validated video analysis by checking for information consistency in the subsequent interviewer notes. Across dyads, 40 misunderstandings were found, with at least one in each interview. Witnesses misunderstood significantly more in the cued recall portion of the interview, while interviewers misunderstood more during free recall. Notably, participants resolved misunderstandings more often than not, used a variety of resolution strategies, and employed more strategies in unresolved instances. Interviewer notes confirmed our resolution analysis in 39/40 instances. The results suggest that the interviewers used creativity, flexibility, and a repertoire of different strategies when misunderstandings occurred.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".